Association-based predictions of pathogen characteristics

ABSTRACT

A system comprising a machine learning classifier trained on a plurality of associations between a host and a pathogen to predict a pathogen characteristic is described herein. The pathogen characteristic can relate to a disease state of the host. Computer-executable instructions for performing a method of forecasting a portion of a target molecule anticipated to influence an organism&#39;s condition also are described herein. The method comprises employing population data to automatically analyze one or more areas of the target molecule to determine the portion of the target molecule anticipated to influence the organism&#39;s condition. The population data can pertain to at least one relationship between at least one diverse organism trait and the target molecule. One or more epitopes forecast by employing the method also are contemplated.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application is a Continuation-in-Part of U.S. patent applicationSer. No. 10/493,165, entitled “A METHOD FOR IDENTIFYING AND DEVELOPMENTOF THERAPEUTIC AGENTS”, filed Sep. 21, 2004, now abandoned, and aContinuation-in-Part of U.S. patent application Ser. No. 10/977,415,entitled “SYSTEMS AND METHODS THAT UTILIZE MACHINE LEARNING ALGORITHMSTO FACILITATE ASSEMBLY OF AIDS VACCINE COCKTAILS,” filed Oct. 29, 2004,now abandoned.

BACKGROUND

Living organisms possess various mechanisms for preventing diseasestates. For instance, some organisms have immune systems that canrecognize proteins on pathogens and tumor cells and subsequentlyneutralize or kill these cells. By way of example, the mammalian immunesystem provides both humoral-mediated and cellular-mediatedimmunological defenses. The humoral arm (e.g., B-cells) manufacturesantibodies that can neutralize invading pathogens and tumor cells. Thecellular arm employs cytotoxic (e.g., CD8+ T cells) and natural killercells to kill cells recognized as foreign or otherwise abnormal.

CD8+ T cells kill infected cells if they recognize short (approximately8-11 amino-acid long) sequences (epitopes) of viral protein inassociation with Major Histocompatibility Complex class 1 (MHC-1)molecules on a cell's surface. These epitopes are generated by normaldigestive processes within the cell and are transported to the cellsurface where they are presented to CD8+ T cells in association withMHC-I molecules. The particular epitopes that can be presented by a celldepend on the type of MHC-I molecules expressed by the organism. Thehuman MHC molecule is sometimes referred to as the Human LeukocyteAntigen (HLA). The major human MHC-1 genes are referred to as HLA-A,HLA-B and HLA-C. HLA genes are the most polymorphic of all human genes.Indeed, hundreds of HLA-A, HLA-B, and HLA-C alleles have been identifiedin the human population.

Pathogenic organisms may sometimes mutate and these mutations may allowthe organism to evade a host's defense systems. Moreover, subsequentexposure to the host's natural defenses or available therapies lead tothe selection of those pathogens most fit to escape the host's naturaldefenses as well as those less susceptible to available treatments.Thus, pathogen evolution may be driven by the selective pressures of thehost's defenses/therapies. Similarly, the vast polymorphisms exhibitedby HLA molecules may be driven by the by co-evolving infectious diseasethreats. This process of evolution and co-evolution is particularlyevident in viruses like the human immunodeficiency virus (HIV), herpesviruses and hepatitis viruses such as hepatitis C virus (HCV).

Various therapies have been directed at augmenting an organism's immunesystem in order to fight disease. By way of example, vaccinations arewidely used to stimulate an immune response to a particular organism(e.g., small pox, polio, etc.) and have even been used to fight tumors(e.g., melanoma). Vaccines may be designed to stimulate humoralimmunity, cellular immunity or both humoral and cellular immunity.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some concepts of the subject matter described herein.This summary is not an extensive overview of the invention. It is notintended to identify key or critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome concepts relevant to the subject matter described herein in asimplified form as a prelude to the more detailed description that ispresented later.

The subject matter described herein provides system and methods thatfacilitate vaccine cocktail assembly via machine learning techniquesthat model sequence diversity. Such assembly can be utilized to generatevaccine cocktails, for instance, directed against species of pathogensthat evolve quickly under immune pressure of the host. For example, thesystems and methods can be utilized to facilitate design of T cellvaccines for pathogens such HIV. In addition, the systems and methodscan be utilized with other applications, such as, for example, sequencealignment, motif discovery, classification, and recombination hot spotdetection.

A resultant vaccine cocktail can be referred to as an “epitome,” or asequence that includes all or many of the short subsequences from alarge set of sequence data, or population. The novel techniquesdescribed herein can provide for improvements over traditionalapproaches that utilize an ancestral sequence from which diversitymushroomed, an average sequence of a population, or a “best” sequence apopulation. For example, vaccine cocktails generated by the systems andmethods described herein can provide for higher epitope coverage andaccount for a large amount of local diversity in comparison with thecocktails of consensus, phylogenetic tree nodes and random strains fromthe data.

In one aspect, a system and/or method that determines epitomes forrapidly evolving pathogens is provided. The system can include an inputcomponent that receives a plurality of patches (e.g., sequences of DNA,RNA, or protein, etc.). Such patches can be a subset or all of apopulation of patches. The received patches can be variable length andconveyed by the input component to a modeling engine. The modelingengine can employ various learning algorithms (e.g.,expectation-maximization (EM), greedy, Bayesian, Hidden Markov, etc.) todetermine the epitome. For example, the modeling engine can determine amost likely epitome, such as, a sequence (e.g., with the greatestcoverage and a shortest sequence for a particular coverage. Upondetermining the epitome, it can be sequenced to create a peptide and/ornucleotide.

In another aspect, systems and methods are provided for designingAIDS/HIV vaccine cocktails. In one instance, the methods includeobtaining AIDS sequence data of contiguous amino acid subsequences(e.g., all possible subsequences with length that corresponds to atypical epitope), building a plurality of disparate sized patches fromthe sequence data by iteratively increasing a size of a patch whiledecreasing an associated free energy (e.g., set equal to zero),aggregating patches to form the AIDS vaccine cocktail by adding a mostfrequent patch during each iteration (unless the patch was alreadyadded). An expectation-maximization (EM) and/or a greedy algorithm canbe utilized to optimize respective iterations.

In another instance, the methods include receiving a plurality of HIVrelated sequences, utilizing the sequences, based on their linear aboutnine-amino acid epitopes (e.g., substantially equally immunogenic), tocreate a compact representation of a large number of HIV relatedpeptides, employing a machine learning algorithm to optimize therepresentation in terms of binding energies, and designing an HIVvaccine cocktail based on the representation. Alternatively, therepresentation can be estimated from the sequence by parsing thesequences into shorter peptides and creating a mosaic sequence that islonger than any individual sequence.

In yet another instance, the systems include a component that receives aplurality of HIV related nine-mers (or 8-11mers), a component thatgenerates a sequence that epitomizes the plurality of nine-mers (or8-11mers), a component that employs a greedy algorithm (e.g.,initialized with a random nine-mer and a variable binding energyestimate) to jointly update a size of the sequence and a free energy,and a component that utilizes the updated sequence to design an HIVvaccine cocktail. Additionally or alternatively, anexpectation-maximization algorithm that concurrently optimizes theupdated sequence and a binding energy can be utilized.

The subject matter described herein can be utilized, for instance, todetermine the influence of variations within a population on the outcomeof disease states (e.g., infections, tumors, etc.) or any othervariable, such as the effect of therapeutic agents (e.g., drugs orvaccines). Such information can be used to design diagnostic andtherapeutic interventions, personalized treatments and/or to determinesusceptibilities.

By way of example, organisms exhibiting genetic polymorphisms can beanalyzed at a population level to derive data useful to predictinteractions between a human gene product and a target molecule. Forinstance, human populations can be typed according to HLA alleles andthis information correlated at the population level with pathogenicpolypeptides. The determined associations can be used to develop newtherapies.

One example of an association is HLA-driven mutation. HLA-drivenmutation is the phenomenon whereby the mutation of a pathogen is notrandom, but rather driven by the HLA type of the host. This enables apathogen to avoid or “escape” that host's immune system. For example, ifa patient has an A*0204 HLA type, the pathogen will favor mutations thatavoid A*0204 epitopes. Correlations between point-wise (single site)mutations in the HIV sequence and the HLA types of infected individualscan be used to pinpoint such associations (e.g., there exists a strongassociation between HLA type B*4402 and the mutation of the virus atposition gp120-13).

By way of example, the relationships can be used to train a machinelearning algorithm to learn a classifier to make predictions. Theassociations may be based, for instance, on Major HistocompatibilityComplex (MHC)-driven mutations of an organism or any other associationbetween a polymorphic human gene and a characteristic of a pathogen. Inone embodiment, the machine learning techniques learn the classifierbased on epitopes that are known to be presented (and known to be notpresented) by particular MHC molecules. The classifier may be used topredict new epitopes and the MHC molecules that present them. Anyclassifier (e.g., logistic regression, neural net, decision tree,support vector machine, etc.) may be used. The predicted epitopes areuseful, for instance, to design vaccine immunogens.

By way of another example, for each HLA-position association, it can beassumed that there is at least one epitope in the vicinity of thatassociation. To look for the most likely epitope that can be recognizedby the immune system of that HLA type, the machine learning classifiercan be applied to each 8-11 long amino-acid sequence in the vicinity ofthe association. A search can be conducted in about a 33-long window oneither side of the association position. Since the positions flanking anepitope can influence whether it is presented on a cell's surface, the33-long window allows for a 12-amino-acid-long flanking region on eitherside of a 9-amino-acid-long epitope.

By way of another example, logistic regression with features selected bythe wrapper method can be used to predict epitopes. Positive examplesinclude the 9-mers obtained from the LANL (hiv-web.lanl.gov) (Los AlamosNational Laboratory) and SYFPEITHI (www.syfpeitbi.de/) (Institute forCell Biology Department of Immunology) databases. Negative examples canbe generated at random from the marginal distribution of amino acidsfrom the positive examples. The features used for prediction comprise:(1) the 2-4 digit HLA of the epitope; (2) the supertype of that HLA; (3)the amino acid at each position in the epitope; and (4) the chemicalproperties of each amino acid at each position in the epitope andconjunctions 1+3, 1+4, 2+3, and 2+4.

A vaccine immunogen can be built by overlapping epitopes andcorresponding flanking regions. For example, the hypothetical immunogenABCDEFGHIJKLM (where each letter denotes an amino acid), is only 13amino acids long, but covers the two 8-long epitopes ABCDEFGH andFGHIJKLM. There are numerous strategies for delivering such immunogenssuch as delivering each sequence in an epitome on its own viral vector,concatenating the sequences in the epitomes and delivering them on asingle viral vector, and/or each sequence can be further subdivided(e.g., to avoid immunodominance), and each component delivered on aseparate viral vector.

By way of example, to determine an epitome for use as an HIV vaccine, aset of epitopes, the HLA molecules that present them and one or moreoptimization criteria are identified. The epitope-HLA pairs can bepreviously known (e.g., hiv-web.lanl.gov/content/immunology/index)and/or can be predicted by epitope prediction techniques. Theoptimization criteria can be selected based on a population of known HIVsequences and a similar population of people who may be infected. Forinstance, the optimization criteria can reflect the idea that if avaccinated person with a given HLA is exposed to a given sequence (orcollection of sequences), only the epitopes in the sequence that are (1)present in the vaccine (or that will cross react with CD8+ T cellsstimulated by the vaccine) and (2) can be presented by an HLA moleculeexpressed by the patient will contribute to immune protection. Oneexample of an optimization criterion is the expected number ofcrossreacting epitopes per patient, where expectation is taken over thegiven population of individuals and the given population of sequences.

There are different models for determining whether CD8+ T cellsstimulated with one sequence will cross-react with another epitope. Oneexample of such a model assumes that there must be an exact matchbetween the sensitizing peptide and the reacting epitope. Anotherexample of a model assumes that the sensitizing peptide and the reactingepitope must differ by at most one conservative amino acid change.

An HIV epitome can be constructed according to the one or moreoptimization criteria by utilizing an optimization algorithm orcombinations of optimization algorithms. Any suitable optimizationalgorithm can be used. For instance, a greedy algorithm that constructsa collection of sequences which together yield a large optimizationscore can be employed. The greedy algorithm can iteratively insert(usually with overlap) a single epitope into the collection of sequencessuch that the optimization score per unit length (where length is thetotal length of all the sequences) increases the most. This procedureproduces a series of epitomes, each with an optimization score and alength. External considerations can be used to choose the optimaltradeoff of score versus length.

The subject matter described herein can provide for improvements overtraditional approaches that utilize an ancestral, average or a “best”sequence of a population. For instance, since consensus models and/orphylogenetic tree models are not well-suited to accounting for the largeamount of diverse strains of HIV, vaccine cocktails generated by thesubject matter described herein can provide for higher epitope coverage.

The following description and the annexed drawings set forth in detailcertain illustrative aspects of the subject matter described herein.These aspects are indicative, however, of but a few of the various waysin which the principles of the invention may be employed and the presentinvention is intended to include all such aspects and their equivalents.For ease of description, HIV has been selected to illustrate how thesubject matter described herein can be employed. However, the subjectmatter so described may be applied to a wide range of analyses includingbut not limited to, for example, herpes virus infections and hepatitisvirus infections.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system to facilitate making predictions.

FIG. 2 illustrates another exemplary system to facilitate makingpredictions.

FIG. 3 illustrates yet another exemplary system to facilitate makingpredictions.

FIG. 4 illustrates an exemplary method for making predictions.

FIG. 5 illustrates an exemplary epitome. The identified row numbers andtheir corresponding SEQ ID NO is provided as follows: Row 1 (SEQ ID NO:176); Row 2 (SEQ ID NO: 177); Row 3 (SEQ ID NO: 178); Row 4 (SEQ ID NO:179); Row 5 (SEQ ID NO: 180); Row 6 (SEQ ID NO: 181); Row 7 (SEQ ID NO:182); Row 8 (SEQ ID NO: 183); Row 9 (SEQ ID NO: 184); Row 10 (SEQ ID NO:185); Row 11 (SEQ ID NO: 186); Row 12 (SEQ ID NO: 187); and Row 13 (SEQID NO: 188).

FIG. 6 is a graph depicting gene coverage versus length.

FIG. 7 is a graph depicting epitope coverage versus length.

FIG. 8 is a map of polymorphism rate at amino acid positions 20-227 ofHIV-1 RT and associations with HLA-A and HLA-B alleles. The known HLA-Aand HLA-B restricted cytotoxic T-lympohocyte (CTL) epitopes (B. T. M.Korber et al., HIV Molecular Immunology Database 1999 (TheoreticalBiology and Biophysics, New Mexico, 1999)) are marked as grey lines inBox A. Box D shows the percentage of patients with a different aminoacid to that in the population consensus sequence at each position inmost recent HIV-1 RT sequence (n=473). The HLA alleles that aresignificantly associated with polymorphism are shown above thepolymorphic residue in Box B, along with the odds ratio (OR) for theassociation. The 15 HLA-specific polymorphisms within the 29 known CTLepitopes restricted to the same broad HLA allele are in grey text andthe five at flanking residues are in black text. Clustered associationsin black text may be within new or putative CTL epitopes. The boxedassociations are those that remain significant after correction fortotal number of residues examined as described in the text. HLA-B*5101is a subtype of HLA-B5, HLA-B44 is a subtype of HLA-B12 and HLA-A24 is asubtype of HLA-A9. In Box C, negative HLA associations are marked withORs expressed as the inverse (1/OR), giving a value >1 for odds of notbeing different to consensus. These are also in grey or black text ifwithin or flanking known CTL epitopes. The known functionalcharacteristics of residues are marked as stability (S), functional (F),catalytic (C) and external (E) adjacent to the residue.

FIG. 9 is a map of polymorphism rate at amino acid positions 95-202 ofHIV-1 reverse transcriptase (RT) and known amino acid functionalcharacteristics. The map of amino acid positions 95-202 of HIV-1 RTshows the percentage of patients with change from population consensusamino acid at each position in pre-antiretroviral treatment HIV-1 RTsequences (n=185). Both conservative (grey bars) or non-conservative(solid black bars) amino acid substitutions are shown.

FIG. 10 shows HIV-RT amino acid sequences in all 52 patients in a cohortof serologically defied HLA-B5 (patients 1-52) compared with populationconsensus sequence. HIV-1 RT sequences are grouped according to theHLA-B subtype of the patient. In all sequences, a dot (.) indicates nodifferences from consensus. Amino acids different to consensus areshown. Where quasispecies with different amino acids were detected, themost common amino acid is shown, except at position 135 where alldetected amino acids in a mixed viral population are shown. All but oneof the forty patients (98%) with the HLA-B*5101 subtype have asubstitution of the consensus amino acid isoleucine (I) at position 135,most commonly with threonin (T). ¹The sequence without I135x is that ofthe single HLA-B*5101 patient who had HAART during acute HIV infection.²This patient did not have molecular genotyping. ³This patient was anHLA-B*5101/B*5201 heterozygote but was counted only once in theHLA-B*5101 group. The four (4) amino acid sequence in line 30corresponds to SEQ ID NO: 189.

FIG. 11 is a map of polymorphism rate at amino acid positions 1-90 ofHIV-1 protease and associations with HLA-A and HLA-B alleles. The knownHLA-A and HLA-B restricted CTL epitopes are marked as grey lines in thetop box. The bottom box shows the percentage of patients with adifferent amino acid to that in the population consensus sequence ateach position in most recent HIV-1 protease sequence (n=493). The HLAalleles that are significantly associated with polymorphism are shownabove the polymorphic residue along with the odds ratio (OR) for theassociation. The HLA-specific polymorphisms within the known CTLepitopes restricted to the same broad HLA allele are in grey text andthe five at flanking residues are in black text. Clustered associationsin black text may be within new or putative CTL epitopes. The boxedassociations are those that remain significant after correction fortotal number of residues examined as described in the text. Negative HLAassociations are marked with ORs expressed as the inverse (1/OR), givinga value >1 for odds of not being different to consensus. These are alsoin grey or black text if within or flanking known CTL epitopes.

FIG. 12A shows the relationship between the degree of viral adaptationto HLA-restricted responses and the HIV viral load.

FIG. 12B shows the frequency distribution of the number of beneficialresidues in each of six vaccine candidates (SIV, clade A virus, clade Cvirus, HXB2 virus, our population consensus virus, and a hypotheticalvaccine) matched to each of the potential incoming infecting viruses ina West Australian population.

FIG. 13 shows the frequency distribution of the estimated strength ofHLA-restricted immune responses that would be induced by each of SIV,clade A virus, lade C virus, HXB2 virus, our population consensus virussequence, and a hypothetical vaccine in response to each of thepotential incoming viruses in a West Australian population using theviral load results as illustrated in the estimated change in viral loadcolumn shown in Table 6.

FIG. 14 illustrates a potential HIV protease therapeutic.

FIG. 15 illustrates a potential HIV RT therapeutic.

FIG. 16 is a schematic illustration of a system that facilitates makinga prediction.

FIG. 17 is a flow diagram illustrating a method of forecasting a portionof a target molecule.

FIG. 18 is a flow diagram illustrating another method of forecasting aportion of a target molecule.

FIG. 19 is a schematic illustration of a system that facilitatesimmunogen design.

FIG. 20 is a flow diagram illustrating a method of determining anepitome.

FIG. 21 is a flow diagram illustrating another method of determining anepitome.

FIG. 22 illustrates an exemplary computing architecture that can beemployed in connection with the subject matter described herein.

FIG. 23 illustrates an exemplary networking environment that can beemployed in connection with the subject matter described herein.

DETAILED DESCRIPTION

The subject matter described herein relates to systems and methods thatutilize machine learning to model sequence diversity to facilitatevaccine cocktail assembly. Suitable machine learning techniques includecost functions, expectation-maximization (EM) and greedy algorithms, forexample. Such assembly can be utilized to generate vaccine cocktails forspecies of pathogens that evolve quickly under immune pressure of thehost. For example, the systems and methods can be utilized to facilitatedesign of T cell vaccines for pathogens such HIV.

The subject matter is described with reference to the drawings, whereinlike reference numerals are used to refer to like elements throughout.In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe subject matter described herein may be practiced without thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form in order to facilitate describing thesubject matter.

As utilized herein, the term “sequence” generally refers to a sequencethat includes all or many of the short subsequences (patches) from alarge set or population of sequence data and/or a sequence whosesubsequences (patches) can be assembled to generate a wide range ofrepresentative sequences of a desired category. Suitable categoriesinclude sequences associated with a specific species, such as HIV,sequences from a specific clade, and/or sequences associated with anacute or chronic phase of infection. Sequences include, for instance,nucleotide sequences (e.g., DNA, RNA) and/or amino acids.

Sequence identity numbers (SEQ ID NO:) included in this specificationhave been prepared using the program PatentIn Version 3.3. Each sequenceis identified in the sequence listing by the numeric indicator <210>followed by the sequence identifier (e.g. <210>1, <210>2, etc.). Thelength, type of sequence and source organism for each sequence areindicated by information provided in the numeric indicator fields <211>,<212> and <213>, respectively. Sequences referred to in thespecification are defined by the information provided in numericindicator field <400> followed by the sequence identifier (e.g. <400>1,<400>2; etc.).

FIG. 1 illustrates a system 100 that determines epitomes (vaccinecocktails) for rapidly evolving pathogens such as HIV. The system 100comprises an input component 110 and a modeling engine 120. The inputcomponent 110 can receive a plurality of patches that can be a subset orall of a population of patches, wherein such patches can be utilized toconstruct an epitome. The received patches can be variable length, forexample, nine-mers, ten-mers, etc. The input component 110 can conveythe patches to the modeling engine 120, which can employ variouslearning algorithms (e.g., expectation-maximization (EM), greedy,Bayesian, Hidden Markov, etc.) that can utilize the patches to determinethe epitome. For example, the modeling engine 120 can be utilized todetermine a most likely epitome. In one instance, the most likelyepitome is defined as the sequence with the greatest coverage. Inanother instance, the most likely epitome is defined as the shortestsequence for a particular coverage. Upon determining the epitome, it canbe utilized to create peptide and/or nucleotide sequencing.

Traditional approaches to designing such vaccines typically modelevolution as a process of random site-independent mutations. However,the environment can affect different pieces of the genome and/orpeptides in a single protein differently. On the population level, thiscan lead to creation of several functional versions of each piece and animpression of immense diversity. In addition, with traditionalapproaches the log mutation scores for sites in a sequence are summedtogether or mutation probabilities are multiplied together to define anumber corresponding to an evolutionary distance between two sequences,when separate pieces commonly have different evolutionary distances. Thenovel approach employed by the system 100 can provide for improvementsover traditional technique via utilizing machine learning techniques. Byway of example, the system 100 can be employed to model sequencediversity to facilitate generating of vaccine cocktails. Such cocktailscan provide for higher epitope coverage in comparison with the cocktailsof consensi, phylogenetic tree nodes and random strains from the data.

FIG. 2 illustrates a system 200 that determines epitomes via a costfunction. The system 200 comprises an input component 210, a modelingengine 220, and a learning component 230. The input component 210 canreceive patches associated with a population and convey the patches tothe modeling engine 120, which can utilize the patches to determine theepitome. The modeling component 220 can employ the learning component230 to facilitate determining the epitome.

By way of example, the learning component 230 can employ a cost function240 to learn the epitome. For instance, the learning component 230 canemploy a cost function that measures the similarity of sequence datawith an estimate of the epitome. By way of example, a set of nucleotideor amino acid patches defined by x={x_(ij)}, wherein i=1, . . . , M (Mis a sequence index) and j=1, . . . , N (N is a site (position) index)can be received by the input component 210 and conveyed to the modelingcomponent 220. The modeling component 220 can utilize the patches toconstruct an M×N matrix/array of sequence data (an epitome) that can beinput to a learning algorithm that renders the epitome as a smallerarray e={e_(mn)} of size Me×Ne, wherein MeNe<<MN. For example, the datacan include 12 sequences (M=12) with lengths of about 42 (N=42), whereasthe epitome size after utilizing the learning algorithm can be reducedto Me=1 and Ne=50. It is to be appreciated that the values utilized inthe above example are illustrative and do not limit the invention.Moreover, it is to be appreciated that the learning algorithm canoptimize the epitome in order to maximize a number of short subsequencesthat are present in the input data, and the input data can be describedby its epitome and a mapping that links the sites in the data to sitesin the epitome.

In order to establish such mapping, the sequence set (patches) x can berepresented as a set of short overlapping subsequences, whereinrespective subsequence x_(S) can include letters from a subset ofsequence positions S. Each index in an index set S generally is twodimensional, pointing both to a sequence and a position within thesequence. These subsequences can be defined on arbitrary biologicalsequences. For example, if X contains M sequences of length N, then thetotal number of contiguous patches in the data of length n is M(N−n)and, thus, the cardinality of S is M(N−n). For each patch x_(S), itsindex set S can be mapped to a hidden set of epitome indices T. In manyinstances contiguous patches x_(S) can be assumed to map to contiguouspatches e_(T) in the epitome so the set T can be identified by the firstindex in the set. A number of possible mappings for each patch aredefined by Me(Ne−n). For HIV amino acid sequence data, thesesubsequences generally are peptides that can correspond to epitopes.With T cell HIV vaccines, the patch length may be equal to the epitopelength (e.g., 8-11 amino acids). However, the context in regionsadjacent to the epitopes can affect HLA binding so the patch length maybe longer, for example, up to about 33 amino acids.

The cost function employed by the learning component 230 to optimize theepitome depends on the application. For example, a cost function thataccounts for various acts that are needed to mount an effective immuneresponse can be utilized, wherein each act can have an associated costin the form of an energy. This energy can be viewed as a negativelog-probability of an event. By way of example, a cost function can beselected to account for the acts utilized to kill an infected cell, forexample, the acts needed for a vaccine e to generate an effective immuneresponse. The vaccine generally is chopped up by cellular mechanisms andshort subsequences (e.g., epitopes) are presented on the surface of theprocessing cell. A positive immune response happens if the clone of thesame T cell can later bind to a virus epitope x_(S) that an infectedcell presents on its surface, initiating the killing of the infectedcell.

In a cell processing a vaccine e, a peptide can be presented on thesurface and bound to a T cell in a process with priming energy E(T). Thepriming energy typically is the sum of the cleavage, HLA binding,transport and/or T cell binding energies, which can influence priming ofan appropriate T cell to attack a cell that presents an epitope patternsimilar to e_(T). In addition, sequence data neighboring an epitope canhave an impact on presentation and, thus, on the priming energy. A Tcell primed with the vaccine epitope e_(T) typically attacks a cell thatpresents a virus epitope x_(S) in a process with attack energy E(x_(S),e_(T)). This energy depends on the cross-reactivity of the T cell. Ifthe patch length is selected so as to account for each epitope plus itsneighboring contextual sequence data, then only a piece of a windowcorresponding to the actual epitope can be utilized to determine theattack energy. The T cell attack energy is lowest when the epitopesubstantially matches the amino acid pattern on the T cell. The energyassociated with priming with e_(T) and attacking x_(S) can be determinedby summing the two energies E(T) and E(x_(S), e_(T)).

In general, for an effective immune response the energy for data set(e.g., many patches from many virus sequences) diversity and/or anability to rapidly evolve can be considered. In particular, the totalenergy typically increases for each patch from the data set that doesnot have a corresponding patch in the epitome that gives a low primingplus attack energy. Equation 1 provides one example of an energy E(x)that satisfies this requirement.

$\begin{matrix}{{E(x)} = {\sum\limits_{S}{\min\limits_{T}{\left( {{E(T)} + {E\left( {x_{S},e_{T}} \right)}} \right).}}}} & \text{Equation~~1:}\end{matrix}$An effective vaccine can be obtained by finding an epitome thatminimizes this energy. It is to be appreciated that Equation 1 isprovided for illustrative purposes and sake of brevity, and does notlimit the invention.

Each of the above energies (E(T) and E(x_(S), e_(T))) can be consideredan energy associated with a stochastic process at equilibrium, whereinthe energy is equal to a negative log-probability of the event orprocess. A suitable priming probability that can be employed inaccordance with the subject invention is defined by Equation 2:p(T)∝exp(−E(T)),  Equation 2:and a suitable attack probability that can be employed in accordancewith the subject invention can be defined by Equation 3:p(x_(S)|e_(T))∝exp(−E(x_(S),e_(T))).  Equation 3:

Exponentiating both sides of the above equations for the total energyE(x) renders Equation 4, which is a probability of the data set x interms of the priming and attack probabilities:

$\begin{matrix}{{{p(x)} \propto {\prod\limits_{S}{\max\limits_{T}\left( {{p\left( x_{S} \middle| e_{T} \right)}{p(T)}} \right)}}},} & \text{Equation~~4:}\end{matrix}$which illustrates an expression that optimizes the epitome viamaximizing the likelihood of independently generating all patches fromthe data set, wherein patch x_(S) is generated from epitome patch e_(T)with probability p(x_(S)|e_(T)) and patch e_(T) is selected from theepitome with probability p(T).

In instances where ΔE(x_(S), e_(T)) is relatively high (e.g., except forsubstantially perfect matches between x_(S) and e_(T)), the total energycan be closely approximated as const −rE, wherein r is the number of thepatches x_(S) that match their corresponding epitome patch e_(T) and Eis the binding energy for such matches. The foregoing can be derived byletting ΔE go to infinity uniformly across mismatches. The const termcan depend on ΔE and/or the total number of patches K, and typicallydoes not depend on the fraction of the matched patches. Thus, for agiven size of the epitome, the quality of the vaccine can depend only onthe percentage of the matched epitopes.

An exemplary functional form that can behave in this manner in the limitinvolves the letter substitution probability θ. This probability can beuniformly or non-uniformly spread over any or all other possibilities(e.g., other three nucleotides in case of DNA/RNA sequence models orother nineteen amino acids in case of protein models) as illustrated inEquation 5:p(x _(S) |e _(T))=θ^(|x) ^(S) ^(≠e) ^(T) ^(|)(1−θ)^(|x) ^(s) ^(=e) ^(T)^(|),  Equation 5:wherein ∥ is the number of elements in the vector argument that aretrue, for example, |x_(S)=e_(T)| is the number of elements on which thetwo patches disagree. When the variability parameter θ can approachzero, an exact match model, which is a conservative choice for vaccinedesign as it limits the assumptions on cross-reactivity, can beutilized. The binding energy model corresponding to this distribution isillustrated in Equation 6:

$\begin{matrix}{E_{x_{S}},{e_{T} = {{- n}\mspace{11mu}{\log\left( {1 - \theta} \right)}}},{\Delta\; E_{x_{S}}},{e_{T} = {{{x_{ij} \neq e_{T{({ij})}}}}\log{\frac{1 - \theta}{\theta}.}}}} & \text{Equation~~6:}\end{matrix}$

With amino acid epitomes, the substitution parameter θ can be defined sothat it decreases the probability of non-conservative amino acidexchange, thus reflecting to some extent the current understanding ofthe T cell cross-reactivity. The θ parameter can also beposition-dependent. It is to be appreciated that there are other ways ofdescribing the position-specific variability. For example, a fullmultinomial distribution over possible letters can be utilized inaccordance with the subject invention. Utilizing this approach, the fullmultinomial distribution over possible letters, such as, for example,θA, θC, θT, θG, wherein θx is the probability of letter x at a givenposition and θA+θC+θT+θG=1 can be employed.

If the epitome is viewed as a stochastic model, the optimizationcriterion can be written as a likelihood of attacking all epitopes x_(S)as illustrated in Equation 7:

$\begin{matrix}{{p\left( \left\{ x_{S} \right\} \right)} = {\prod\limits_{S}{\sum\limits_{T}{{p\left( x_{S} \middle| e_{T} \right)}.}}}} & \text{Equation~~7:}\end{matrix}$Under a conservative assumption, wherein θ is approximated to equal toone, this cost can become equivalent to the epitome's coverage ofsubstantially all virus epitopes. If the cost is defined in terms of thetotal energy barrier summed over substantially all virus epitopes x_(S),then the free energy can be defined as illustrated in Equation 8:

$\begin{matrix}{{F = {\sum\limits_{S}{\sum\limits_{T}{{q\left( T \middle| S \right)}\log\frac{p\left( T \middle| S \right)}{{p\left( x_{S} \middle| e_{T} \right)}{p(T)}}}}}},} & \text{Equation~~8:}\end{matrix}$which combines the binding energies described above via an auxiliarydistribution q(T|S) for each data patch S.

Individual patch energies −log p(x_(S)|e_(T))−log p(T) can be summed toform an estimate of the total energy barrier to the immunity against allforms of the virus if the mapping variable T is known for each sequencefragment S. However, with some probability any piece of the epitome canbe chopped and presented by cellular mechanisms and utilized to prime anappropriate T cell, which could later, as a memory cell, bind to anarbitrary HIV patch x_(S). Thus, similar segments of the epitome canpotentially represent a substantially similar antigen x_(S). Thedistribution over the epitome correspondence is expressed throughq(T|S). In order to compute the average energy over all mappings, anintegration under q as a measure of posterior probability of matchingthe data epitopes to the appropriate epitome patches can be employed. Inaddition, if the epitome has multiple patches that represent some dataepitope x_(S), such epitome can be more effective than an epitome thathas only one way of providing adaptive immunity to this epitope. Thus,the entropy of the distribution q offsets the binding energy, and thefree energy of the epitome sequence can be expressed as above. It is tobe appreciated that although the epitome and the viruses can go throughsubstantially similar acts, there is no total symmetry of S and T inEquation 8 when optimizing targeting all likely targets S in the virusinstead of optimizing the intersection between epitome and a set ofviruses.

The free energy minimum can be equal to the negative log likelihood asillustrated in Equation 9:

$\begin{matrix}{{{- \log}\mspace{11mu}{p\left( \left\{ x_{S} \right\} \right)}} = {\arg\mspace{11mu}{\max\limits_{q}{F.}}}} & \text{Equation~~9:}\end{matrix}$Maximizing the likelihood with respect to the epitome e can beequivalent to minimizing the free energy with respect to the posteriordistributions q(T|S) for all S and the epitome e. A suitable assignmentin the posterior distribution q can require an exact match (e.g., θ=0).

It is to be appreciated that some epitopes are known, but many are not.By studying the escapes in genes, by using databases of epitopes thatare known to be immunogenic for some HLA types, or by studying theMHC/cleavege/transport binding data, the probability p(S) can beassociated with each peptide x_(S) in the data, for example, accordingto how likely the observed pattern is to be presented on the surface ofthe infected cell, which is the prerequisite for the T cell immunity. Ifa peptide is not going to be presented, it needs not be included in theepitome and the free energy is defined as illustrated in Equation 10:

$\begin{matrix}{F = {\sum\limits_{S}{{p(S)}{\sum\limits_{T}{{q\left( T \middle| S \right)}\log{\frac{p\left( T \middle| S \right)}{{p\left( x_{S} \middle| e_{T} \right)}{p(T)}}.}}}}}} & \text{Equation~~~10:}\end{matrix}$Utilizing a conservative assumption (as discussed above), the vaccineoptimization algorithm can be defined by Equation 11:

$\begin{matrix}{e = {\lim\limits_{\theta\rightarrow 0}\;{\arg\mspace{11mu}{\min\limits_{e}\mspace{11mu}{\min\limits_{q}{F.}}}}}} & \text{Equation~~~11:}\end{matrix}$

FIG. 3 illustrates a system 300 that determines epitomes via anexpectation-maximization (EM) algorithm. The system 300 comprises aninput component 310, a modeling engine 320, and a learning component330. The input component 310 can receive patches and convey them to themodeling engine 320, which can utilize the sequences to determine theepitome. The modeling engine 320 can employ the learning component 330,which can utilize a cost function 340, an EM algorithm 350, and/or agreedy algorithm 360. The modeling engine 320 can employ the EMalgorithm 350 to facilitate determining the epitome. For example, byconsidering the size of the epitome as prescribed (e.g., by vaccine thedelivery constraints) and utilizing an initial random guess for theepitome parameters, the above can be performed via an iterativeoptimization by utilizing the EM algorithm 350.

By way of example, for each x_(S) the posterior distribution q ofpositions T can be estimated by Equation 12:

$\begin{matrix}{{q\left( T \middle| S \right)} = {\frac{{p\left( {x\; S} \middle| {e\; T} \right)}{p(T)}}{\sum\limits_{T}{{p\left( {x\; S} \middle| {e\; T} \right)}{p(T)}}}.}} & \text{Equation~~~12:}\end{matrix}$The epitome that minimizes the free energy can be re-estimated asillustrated in Equation 13 and Equation 14:

$\begin{matrix}{{e_{mn} = {\arg\mspace{11mu}{\max\limits_{e_{mn}}{\sum\limits_{{T{(i)}} = {({m,n})}}{{q\left( T \middle| S \right)}\left\lbrack {x_{s{(i)}} = e_{mn}} \right\rbrack}}}}},{and}} & \text{Equation~~~13:}\end{matrix}$

$\begin{matrix}{\theta = {\frac{\sum\limits_{m,n}{\sum\limits_{s}{{p(S)}{\sum\limits_{{T{(i)}} = {({m,n})}}{{q\left( {T\text{❘}S} \right)}\left\lbrack {x_{s{(i)}} \neq e_{m\; n}} \right\rbrack}}}}}{\sum\limits_{m,n}{\sum\limits_{s}{{p(S)}{\sum\limits_{{T{(i)}} = {({m,n})}}{q\left( {T\text{❘}S} \right)}}}}}.}} & \text{Equation~~~14:}\end{matrix}$Iterating these equations is an expectation maximization (EM) algorithmfor the epitome model, which reduces the free energy in each act, thusconverging to the local minimum of the free energy and the local maximumof the likelihood.

The EM algorithm 350 can jointly and concurrently optimize both theepitome and the binding energy parameters θ. The algorithm can beinitialized with a random epitome and a relatively large variabilityestimate θ. After several iterations, θ generally decreases as theepitome starts to more closely match the data and the uncertaintycontracts. The energy barrier ΔE_(x) _(s) _(,e) _(T) to non-exactmatches can become relatively steep capturing the conservativeassumption on high T cell specificity. If the epitome is not longenough, then the algorithm decreases the allowed variability (and thusincreases specificity) to a level where the balance between covering allthe data and allowing for as little cross-reactivity as possible isreached for the assumed energy model. The variability can be furtherdecreased to force the model to fit as many patches as possible withoutany latitude on cross-reactivity. It is to be appreciated that variousother algorithms such as the greedy algorithm, Hidden Markov model,neural network, and/or Bayesian-based algorithms can be utilized inaccordance with an aspect of the subject invention. For example, thegreedy algorithm can be utilized to jointly update the size of theepitome sequence or sequences and the free energy in a greedy fashion.

Optionally, an intelligence component 370 can be employed in accordancewith an aspect of the invention. In one instance, the intelligencecomponent 370 can be utilized to facilitate determining which learningalgorithm to employ. For example, the machine learning component 360 canprovide various cost functions, expectation-maximization algorithms,greedy algorithms, etc. as described above. The intelligence component370 can determine which algorithm(s) should be employed, for example,based on a desired vaccine, a set of input patches, epitope length, etc.In addition, the intelligence component 370 can perform a utility-basedanalysis in connection with selecting an algorithm to utilize, withdetermining an epitome, and/or with optimizing an epitome.

In another aspect of the invention, the intelligent component 370 canperform a probabilistic and/or statistic-based analysis in connectionwith inferring and/or determining a suitable machine learning algorithmand/or an epitome. As utilized herein, the term “inference” andvariations thereof refer generally to the process of reasoning about orinferring states of the system, environment, and/or user from a set ofobservations as captured via events and/or data. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states, for example. The inference can beprobabilistic—that is, the computation of a probability distributionover states of interest based on a consideration of data and events.Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether or not the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classification(explicitly and/or implicitly trained) schemes and/or systems (e.g.,support vector machines, neural networks, expert systems, Bayesianbelief networks, fuzzy logic, data fusion engines . . . ) can beemployed in connection with performing automatic and/or inferred actionin connection with the subject invention.

FIG. 4 illustrates a methodology 400 that determines epitomes forpathogens such as HIV. For simplicity of explanation, the methodology isdepicted and described as a series of acts. It is to be understood andappreciated that the present invention is not limited by the actsillustrated and/or by the order of acts, for example acts can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts may berequired to implement the methodology in accordance with the presentinvention. In addition, those skilled in the art will understand andappreciate that the methodology could alternatively be represented as aseries of interrelated states via a state diagram or events.

At 410, a plurality of patches, or sequences, which can be a subset orall of a population of sequences, is received. Such patches can bevariable length, for example, nine-mers, ten-mers, etc. At 420, variouslearning algorithms can be utilized to determine the epitome, based onthe received sequences. For examples, learning algorithms such as a costfunction (as described herein), an expectation-maximization (EM)algorithm (as described herein), a greedy algorithm, Bayesian models,Hidden Markov models, neural networks, etc. can be employed inconnection with various aspect of the subject invention. It is to beappreciated that the resultant epitome can be a most likely epitome suchas an epitome that includes a sequence with the greatest coverage, ashortest sequence for a particular coverage, etc. At reference numeral430, the epitome can be output. It is to be appreciated that such anepitome can be utilized to create peptide and/or nucleotide sequencingto generate an AIDS vaccine cocktail. This novel approach can providefor improvements over traditional techniques by modeling sequencediversity through machine learning. Resulting vaccines (for HIV) canprovide for higher epitope coverage in comparison with the cocktails ofconsensi, phylogenetic tree nodes and random strains from the data.

FIG. 5 illustrates an exemplary epitome 500 and a plurality of patches(sequences) 510 that the epitome 500 epitomizes in terms of linearnine-amino acid epitopes, assuming that all nine-mers are equallyimmunogenic and exposure to the immune system leads to nocross-reactivity. Although nine-mers are depicted, it is to beappreciated that essentially any mer (e.g., ten-mers, eleven-mers, etc.)can be utilized in various aspects of the subject invention, and any orall assumptions can be relaxed. As illustrated at 520, 530, 540 and 550,three portions of the epitome 500 can be matched with various portionsof the plurality of sequences. Such matching can be achieved by moving awindow (e.g., nine-long, as depicted in FIG. 5) over the epitome, forexample, from left to right. While moving the windowing, the window canbe matched with a corresponding sequence epitopes. The epitome 500 canbe estimated from the data by chopping up the input sequences 510 intoshort peptides of epitope length or longer and creating a mosaicsequence longer than any given data sequence, but much shorter than thesum of all input sequence lengths. It is to be appreciated that eventhough it may be desirable to achieve coverage of short epitopes, due tothe overlaps in these epitopes in the data, the epitome may favorconservation of long amino acid stretches from the epitomized sequences.Therefore, the epitome can also be viewed as a collection of longer orshorter protein pieces needed to compose each of the given sequences.

FIG. 6 depicts a graph 600 that illustrates epitome coverage of aplurality of different CAG genes over length, and FIG. 7 depicts a graph700 that illustrates epitome coverage of various epitopes of a GAG geneover length. In these figures, respective axes 610 and 710 correspond tocoverage as a function of percent and respective axes 620 and 720corresponds to length. In this example, epitomes of size 1×Ne can beutilized. However, as a vaccine the epitome may need to be delivered ina different format, which can be achieved by chopping the 1×Ne epitomeinto smaller pieces or directly optimizing an epitome of a requiredformat as described herein. The patches derived from the sequence datacan include all possible contiguous amino acid subsequences, forexample, of size nine, corresponding to the length of a typical epitope,with indices S=11, 12, . . . , 19. In order to include a context thatcan affect escape, the patches may need to be longer. However,optimizing for coverage of shorter patches can lead to preservation of alarger context around any or all patches due to patch overlaps both indata and in the epitome. To compute various vaccine components, anexpectation-maximization (EM) algorithm, a greedy algorithm, and thelike can be utilized to train a mixture of profile sequences, forexample, sequences in which each site has an associated most likelyletter and a probability of generating any other letter.

Epitomes of various sizes can be utilized, wherein such epitomes can beconstructed by iteratively increasing the size of the epitome anddecreasing the free energy with the assumption θ=0, thus increasingcoverage of the epitopes from the data. Respective acts can be optimalincremental moves, for example, by adding a most frequent data patchthat is not yet included in the epitome. This optimization follows aconservative assumption that none of the epitopes in the sampled virusesshould be apriori ignored in an effective vaccine (e.g., p(S)=const) andonly an exact copy of epitope in the vaccine will lead to an effectivevaccine (θ=0). Thus, the efficiency of the optimization algorithms canbe evaluated by a percentage of the data patches that are exactly copiedin the epitome. As discussed previously, coverage is related to the freeenergy and can be more intuitive when θ=0.

FIGS. 8-15 show the results of techniques for obtaining data at thepopulation level and for the development of therapeutics in the contextof HIV-1 proteins, such as HIV-1 reverse transcriptase (RT) (which ishighly expressed in virions and immunogenic in the early response toHIV-1). HIV-1 RT may be substituted for another suitable HIV protein orthe sequences selected for examination may be derived from another virusor organism.

The Western Australian (WA) Cohort Study was established in 1983 as aprospective observational cohort study of HIV infected patients. From1983 to 1998, the study captured data from 80% of all HIV-infected casesand all notified AIDS cases in the state of Western Australia.Comprehensive demographic and clinical data was and is collected atoutpatient and in-patient visits by medical staff and entered into anelectronic database. Start and stop dates of all antiretroviraltreatments are recorded. Routine laboratory test results areautomatically downloaded from the laboratory directly into the cohortdatabase. Data from a maximum of 473 cohort subjects with HLA and viralsequence data were analyzed in logistic regression models.

The vast majority of patients in the cohort reside in or near thecapital of Western Australia, Perth, which is one of the mostgeographically isolated cities in the world. New HIV-1 infections aremost frequently acquired from sources within Western Australia (53.3%)or other states in Australia (24.3%), and less commonly from Asia(8.2%), Africa (5.1%), Europe (4.9%), North America (3.4%) or SouthAmerica (0.8%). Participants have certain demographic, clinical andlaboratory data collected routinely, including HLA class I serologicaltyping and HLA class II sequence based typing. HIV-1 RT proviral DNAsequencing is performed at first presentation (prior to anyantiretroviral treatment in 185 cases) and serially while on RTinhibitor therapy. This study encompasses data collected overapproximately 2210 patient-years of observation.

Relationships between HIV-1 RT sequences in 473 participants of theWestern Australian (WA) HIV Cohort Study and their HLA-A, -B and -DRB1genotypes were examined. The HLA-A and -B alleles present in individualsincluded A1, A2, A3, A9, A10, A11, A19, A28, A31, A36, B5, B7, B8, B12,B13, B14, B15, B16, B17, B18, B21, B22, B27, B35, B37, B40, B41, B42,B55, B56, B58, B60 and B61.

All HLA-A and HLA-B broad alleles were typed by microcytotoxicity assayusing standard NIH technique. For this study, 51 HLA-B5 individuals and57 HLA-B35 individuals had HLA-B sequence amplified using primers to thefirst intronic dimorphism as previously described (see for example N.Cereb and S. Y. Yang, Tissue Antigens 50, 74-76 (1997)) and productswere sequenced by automated sequencing. HLA-DRB1 alleles were typed bysequencing using previously reported methods (see for example, D. Sayeret al., Tissue Antigens 57, 46-54 (2001)).

HIV-1 DNA was extracted from buffy coats (QIAMP DNA blood mini kit;Qiagen, Hilden, Germany) and codons 20 to 227 of RT were amplified bypolymerase chain reaction. A nested second round PCR was done and thePCR product was purified with Bresatec purification columns andsequenced in both forward and reverse directions with a 373 ABI DNASequencer. Raw sequence was manually edited using software packagesFactura and MT Navigator (PE Biosystems).

The viral load assay used until November 1999 was the HIV Amplicor™(Roche, Branchburg, USA, lower limit of detection 400 copies/mL). TheRoche Amplicor HIV monitor Version 1.5, Ultrasensitive, lower limit ofdetection 50 copies/mL was used thereafter. Viral load assays wereroutinely performed at least three monthly in all patients.

Using the WA HIV Cohort Study database to facilitate analyses based onFisher's exact tests and logistic regression models standard formulaewere used for power calculations (see for example J. H. Zar, inBiostatistical Analysis, Bette Kurtz, Ed. (Prentice-Hall International,New Jersey, 1984) chap. 22.11). Individual covariates were assessedseparately for association with polymorphism at the amino acid positionunder consideration using Fisher's exact test, and only those withunivariate P-values≦0.1 were included in further analyses. If the numberof covariates selected by this method exceeded 10% of the patientnumbers a forward stepwise procedure based on standard logisticregression was used to reduce the number to 10% and standard backwardselimination used until all covariates had a P-value≦0.1. For example,covariates were assessed separately for association with I135 usingFishers exact test, and only those with univariate P-values≦0.1 wereincluded in further analyses. The removed alleles were A1, A2, A3, A9,A11, A19, A28, B7, B8, B13, B14, B15, B16, B21, B22, B27 and B35.

Since the number of covariates selected at position I135 was less than10% of the number of patients, no forward selection was needed. Astandard backwards elimination was then carried out at position I135.The covariant with the largest P-value was removed and the logisticmodel refitted. This was repeated until all covariates had a P-valueless than 0.1, thus removing HLA alleles B12, B17 and B40.

To accommodate relatively small samples in some of the logisticregressions, exact P-values were based on randomization tests ratherthan the usual large sample approximations (see for example F. L. Ramseyand D. W. Schafer, in The statistical sleuth. A course in methods ofdata analysis, (Duxbury Press, 1997), chap. 2). In this procedurecovariate sets were randomly permuted amongst the patients and thestandard test values for association with polymorphism calculated foreach permutation. This procedure generated 1000 random permutations foreach model and based the P-value on the appropriate percentage of testvalues more extreme than that pertaining to the actual data.P-values≦0.05 were considered to be significant using this method. Forexample, at position I135, alleles HLA-A10 and -B18 were removed,leaving HLA-B5 as the significant association with I135.

Analyses were conducted to determine the probability of finding bychance at least fifteen significant positive associations withincorresponding known cytotoxic T lymphocytes (CTL) epitopes. Ifsignificant associations were occurring randomly across residues, theprobability that an HLA association would occur within the known CTLepitope restricted to that allele equates to the relative proportion ofall residues falling within the epitope. The total number of significantassociations within known epitopes is then a sum of non-identicalbinomial variables, whose distribution can be evaluated via simulation,for example. Only 4.27 significant positive associations within knownepitopes were expected based on the random hypothesis compared with the15 observed (approximate P-value <0.001).

Correction factors for multiple comparisons were generated as describedlater and corrected exact P-values were determined by the function:1−(1−P)^(x) where x=correction factor. The overall P-value for allassociations at all positions was obtained by considering theextremeness of the sum of the individual tests at each position relativeto the values of this sum obtained from the randomization data sets. Forthe Cox proportional hazards models of viral load, HLA associations hadto have at least four individuals representing HLA allele versus non-HLAallele, with polymorphisms and without to be included (n=106). The viralload measured closest to first pre-treatment HIV-1 RT sequencing wasused.

To determine whether polymorphisms in HIV-1 RT sequences in the studypopulation were distributed randomly or occurred at preferred sites, thepopulation consensus sequence was used as a reference sequence and wasdetermined by assigning the most common amino acid at each position from20 to 227 (numbering system as in reference B. T. M. Korber et al., HIVMolecular Immunology Database 1999 (Theoretical Biology and Biophysics,New Mexico, 1999)) of all first HIV-1 RT amino acid sequences prior toany antiretroviral therapy (n=185). This population consensus sequencematched the lade B reference sequence HIV-1 HXB2 (L. Ratner et al.,Nature 313, 277-284 (1985)) at all positions in RT except 122 (lysineinstead of glutamate) and 214 (phenylalanine instead of leucine). Thepercentages of patients with a different amino acid in their own firstpre-treatment HIV-1 RT sequence compared to that of consensus sequencewere calculated for each residue. The relationship between thispolymorphism rate and the functional characteristics (stability,functional, catalytic or external) known for amino acids betweenpositions 95 to 202 in HIV-1 RT was examined.

The rate of polymorphism at single residues was highly variable, rangingfrom 0% to 60% and appeared to correlate with the expected viraltolerability of change at that site. For example, the polymorphism ratesat the three critical catalytic residues in HIV-1 RT (0.53%), stabilityresidues (n=37, 1.06%) and functional residues (n=11, 3.05%) were lowerthan at external residues (n=10, 5.95%) (P=0.0009, Wilcoxon).

As antigen specific CTL responses are HLA class I restricted,polymorphisms in HIV-1 RT that were the result of CTL escape mutationwere examined to determine whether they would be HLA class Iallele-specific across the population and would be in residues within orproximate to CTL epitopes. The relationship between HLA-A and HLA-Bbroad alleles (as explanatory covariates) and polymorphism in HIV-1 RT(as the outcome or response variable) in multivariate logisticregression models was therefore examined. The most recent HIV-1 RTsequence in each patient was used in these analyses (n=473). Singleamino acid residues in HIV-1 RT were examined in separate models. Anindividual model at one residue determined the statistical significanceof association(s) between the covariates (HLA alleles) and the outcome(polymorphism at that residue only) and gave odds ratios (ORs) forassociations.

The statistical power to detect the effect of any individual HLA allelein these models depended on the frequency of the allele in thepopulation and the frequency of polymorphism at the amino acid positionbeing examined. An initial power calculation was performed for eachposition to determine for which alleles there was a reasonable power todetect an association if it existed (at least 30% power to detect anOR>2.0 or <0.5). Only those HLA alleles that had a univariateassociation with polymorphism with P≦0.1 were examined at each viralresidue (one to ten HLA alleles, mean 3.15 at 72 positions) insubsequent analyses. Final covariates in the logistic regression modelsalso withstood a standard forward selection and backwards eliminationprocedure. Permutation tests based on the logistic models were used todetermine the exact P-values for associations (F. L. Ramsey and D. W.Schafer, in The statistical sleuth. A course in methods of dataanalysis, (Duxbury Press, 1997), chapter 2).

HLA alleles with less than 30% power were removed. The removed allelesat position 135 were A31, A36, B42, B55, B56, B58 and B61. It isimportant to note that there was less power to detect negativeassociations than positive associations. For example, at the mean HLAfrequency of 10.9 and mean polymorphism rate of 4.0%, there was 30%power to detect an OR of 2.0 (i.e. a positive association) but only 5.6%power to detect an equivalent negative OR of 0.5.

The results of all the individual models were plotted together on a mapof HIV-1 RT amino acid sequence from position 20 to 227. There were 64positive associations (i.e., OR>1) between polymorphisms of singleresidues in HIV-1 RT and specific HLA-A or -B alleles (P≦0.05 in allcases). Polymorphisms specific for a particular HLA allele clusteredalong the sequence. For example, HLA-B7 was associated with polymorphismat positions 158 (OR=4), 162 (OR=10), 165 (OR=2) and 169 (OR=13), whichare all within or flanking the known HLA-B7 restricted CTL epitopeRT(156-165) (C. M. Hay et al., J Virol 73, 5509-5519 (1999); L.Menendez-Arias, A. Mas, E. Domingo, Viral Immunol 11, 167-181 (1998); C.Brander and B. D. Walker, in HIV molecular immunology database, B. T. M.Korber et al., Eds. New Mexico, (1997)). There was also clustering ofassociations for HLA-B12 (at positions 100 and 102, 115 and 118, 203 and211), HLA-B35 (121 and 123), HLA-B18 (at 135 and 142), and HLA-B15 (at207, 211 and 214).

Fifteen HLA class I allele-associated polymorphisms occurred at residueswithin the 29 CTL epitopes that are characterized, published and knownto be restricted to those alleles. Four of these residues (101, 135, 165and 166) were at primary anchor positions within CTL epitopes (HLA-A3(C. Brander and P. J. R. Goulder, in HIV Molecular Immunology 2000, B.T. M. Korber et al., Eds. (Theoretical Biology and Biophysics, NewMexico, 2000) chap. Part 1. Review Articles), HLA-B51 (L.Menendez-Arias, A. Mas, E. Domingo, Viral Immunol 11, 167-181 (1998); N.V. Sipsas et al., J Clin Invest 99, 752-762 (1997))/HLA-B*5101 (H.Tomiyama et al., Hum Immunol 60, 177-186 (1999)), HLA-B7 (C. M. Hay etal., J Virol 73, 5509-5519 (1999); L. Menendez-Arias, A. Mas, E.Domingo, Viral Immunol 11, 167-181 (1998); C. Brander and B. D. Walker,in HIV molecular immunology database, B. T. M. Korber et al., Eds. NewMexico, (1997)) and HLA-A11 (Q. J. Zhang, R. Gavioli, G. Klein, M. G.Masucci, Proc Natl. Acad. Sci U.S.A 90, 2217-2221 (1993)) restrictedrespectively) where mutation could abrogate binding to the HLA molecule.The remaining 11 associations were at non-primary anchor positions ofpublished CTL epitopes. There were a further five HLA allele-specificpolymorphic residues that flanked CTL epitopes restricted to the sameHLA alleles. The residues at positions 26 and 28 that flank known HLA-A2and HLA-A3 restricted epitopes were predicted proteosome cleavage sites(C. Kuttler et al., J Mol Biol 298, 417-429 (2000)). If significantpositive associations occurred randomly across residues only 4.18 wouldhave been expected to fall within corresponding known CTL epitopes. Theobserved number of 15 was significantly higher than this (P<0.0004).Furthermore, an excess of associations over that expected was seen forten of the 11 HLA specificities with epitopes in this segment of HIV-1RT.

A final set of analyses was conducted to identify which of thesesignificant HLA associations would remain significant after a correctionfor the effective number of independent comparisons made over the entireanalysis. HLA genotypes were randomly reassigned amongst individuals andthe previously described analysis was run 1000 times to determine thenumber of false positive associations expected by chance alone for eachHLA allele. The average number of P-values≦0.05 obtained was multipliedby 20 (i.e., 1/0.05) to estimate the effective number of independenttests carried out as a correction factor for multiple comparisons foreach HLA allele. Correction factors ranged from 5.0 (HLA-B37) to 92.2(HLA-B7) for positive associations and 0.8 to 42.8 for negativeassociations. There were 14 associations that still had a P≦0.05following this correction.

The randomization data sets were also used to generate an overall testof significance, taking multiple comparisons into account, of all HLAassociations at all positions across all models. This test had a P-valueof <0.001.

Molecular HLA sub-typing can increase strength of association betweenpolymorphism and HLA alleles. Serologically defined HLA class I alleleshave subtypes, defined by high resolution DNA sequence based typing,that have amino acid sequence differences in the peptide binding regionsthat influence epitope binding. For these alleles, it would be expectedthat CTL escape mutation would be more closely associated with themolecular subtype than with the broad HLA allele.

As examples, two strong associations with broad HLA alleles withwell-represented splits, at sites within known CTL epitopes, and wherethe HLA restriction of the epitope at the molecular level was known wereexamined. Polymorphism at position 135 (I135x, where I is the consensusamino acid isoleucine and x is any other amino acid) associated withpresence of HLA-B5 was the strongest positive HLA association at aresidue within a published epitope (OR=17, P<0.001). D177x, within anepitope specifically restricted to the HLA-B*3501, was associated withHLA-B35 (OR=4, P<0.001).

Isoleucine is the amino acid at position 135 of the consensus HIV-1 RTsequence. It is the eighth amino acid and anchor residue of a known 8merHLA-B5 (*5101) restricted CTL epitope, RT(128-135 IIIB). Six of theother seven amino acid residues of the epitope are critical stabilityresidues for the RT protein and are relatively invariant in the cohort.Of all 52 HLA-B5 positive patients, 44 (85%) had a substitution ofisoleucine at position 135. Of the 421 non-HLA-B5 individuals, only 123(29%) had this change (P<0.0001, Fisher's exact test).

DNA sequencing to subtype all 52 individuals in the cohort with theHLA-B5 allele was undertaken. One HLA-B5 patient did not have sufficientDNA sample to perform high resolution HLA typing. Forty of the remaining51 HLA-B5 patients were of the HLA-B*5101 subtype. All but one of these40 HLA-B*5101 patients (98%) had I135x (I135T in 25 cases, I135V in 5cases, I135L/M/R or mixed species in the remaining 9 cases). Incontrast, only 127 of the 432 (29%) non-HLA-B*5101 patients in thecohort had I135x (P<0.0001, Fisher's exact test). For the most commonsubstitution, from isoleucine to threonine, the predicted half time ofdissociation score for the mutant epitope (TAFTIPST) is 11 compared with440 for the consensus sequence (TAFTIPSI), indicating that binding tothe HLA molecule in vivo is abrogated. This substitution has been shownto necessitate a hundred-fold increase in the peptide concentrationrequired to sensitize target cells for 50% lysis (SD₅₀) by CTLs in vitro(N. V. Sipsas et al., J Clin Invest 99, 752-762 (1997)). The less commonisoleucine to valine substitution at position 135 has been associatedwith a ten-fold increase in SD₅₀ compared with consensus epitope (N. V.Sipsas et al., J Clin Invest 99, 752-762 (1997)).

The single HLA-B*5101 patient who was not different to consensus atposition 135 was a patient who had highly active antiretroviral therapy(HAART) administered during acute HIV seroconversion. The patient hadpresented within days of virus transmission with plasma HIV RNAconcentration (viral load) of 6.5 log copies/mL and a negative HIVantibody test. He had no symptoms of seroconversion illness. After HAARTwas started, viral load progressively decreased to undetectable levelsover the next six months, and has remained undetectable on treatment fora further ten months until the present time.

The one patient with the HLA-B*5108 subtype, and four of eight patientswith the HLA-B*5201 subtype did not have I135x, suggesting that thesesubtypes may not bind the RT(128-135 IIIB) epitope. Both subtypes differfrom HLA-B*5101 by only two amino acids (HLA-B*5108 at positions 152 and156, HLA-B*5201 at positions 63 and 67, of HLA amino acid sequence)(IMGT/HLA sequence database; www.ebi.ac.uk/imgt/hla). The remaining twopatients were shown to be HLA-B*5301 by sequencing.

The HLA-B35 subtype HLA-B*3501 only differs from HLA-B*3502, -B*3503,-B*3504 by one or two amino acids in the peptide binding region and yetthe different epitope specificities of these subtypes have a strikingeffect on risk of clinical progression of HI V-I infection. The epitopeRT(175-183) binds to HLAB*3501 and contains a binding motif that isdistinct to that predicted for other HLAB35 subtypes(www.uni-teubingen.de/uni/kxi/). Of 57 HLA-B35 positive individuals inthe study population, 26 (46%) had D 177x compared with 84 of 416 (20%)non-HLA-B35 individuals (P<0.0001, Fisher's exact test). However, therewere 19 of 33 (58%) HLA-B*3501 patients that had D177x compared with 86of the 440 (20%) non-HLA-B*3501 patients (P<0.0001, Fisher's exacttest). Thus, the univariate relative risk of polymorphism increased from2.7 to 4.7 after the molecular subtype of HLA-B35 was considered. Thisanalysis was repeated for other HLA-B35 associated polymorphisms inHIV-I RT, I69x, D121x and D123x and in all cases, the association wasstrengthened by considering molecular subtypes of HLA-B35.

To determine whether selection of HLA-specific polymorphisms over timewas demonstrable, the amount of HLA-specific variation present in themost recent HIV-1 RT sequence with the first sequence for allindividuals was examined. For 61 of 64 HLA-specific polymorphisms, thenumber of individuals with a specific amino acid polymorphism increasedover time and under observation. In 52 of these cases, the increase wassignificantly greater in those with the HLA allele associated with thepolymorphism, compared with all others without the allele (P=0.0008,sign test) as shown in Table 1.

TABLE 1 Polymorphism Number (n) P-value (sign test) HLA-specificpolymorphisms 64 P < 0.0001 HLA-specific polymorphisms that 61 P <0.0001 increase from first to last HIV-1 RT sequences HLA-specificpolymorphisms that 52 P < 0.0001 increase from first to last HIV-1 RTsequences in those with the corresponding allele compared with allothers

Primary CTL escape mutation in an HIV-1 p24 epitope has been shown toinduce possible compensatory mutations in the virus. To determinewhether the secondary or compensatory changes accompanying primary(putative) CTL escape mutation were evident at a population level,polymorphisms were included at all ‘other’ positions in HIV-1 RT, alongwith HLA alleles, as covariates in all multivariate logistic regressionmodels. All but two of the 64 positive HLA-specific polymorphisms werealso associated with one or more polymorphisms at other positions.

In the multiple logistic regression models described earlier, there were25 residues at which polymorphism was HLA-specific but with an OR<1,indicating a ‘negative’ association. For example, change from consensusamino acid at positions 32, 101, 122, 169, and 210 of HIV-1 RT wasnegatively associated with presence of HLA-A2 (P≦0.05 in all cases).This means that HLA-A2 individuals were significantly less likely tovary from the consensus at these sites compared with all non-HLA-A2individuals in the cohort. The negative ORs were inversed (1/OR) to givea value >1 for the odds of not having a polymorphism. HLA-A2 is the mostcommon HLA-A allele in our cohort and had five of the 25 negativeassociations (compared with three of the 64 positive associations).

Similarly, individuals with HLA-B7 were more likely to have theconsensus amino acid at positions 118, 178 and 208 compared withnon-HLA-B7 individuals. According to this analysis there was less powerto detect negative associations than positive associations. For example,at the mean HLA frequency of 10.9 and mean polymorphism rate of 4.0%,there was 30% power to detect an OR of 2.0 (i.e. a positive association)but only 5.6% power to detect an equivalent negative OR of 0.5.

As HIV-1 viral load has been shown to be inversely proportional toHIV-specific CTL responses, studies were undertaken to determine whetherthe presence of putative CTL escape mutations was associated withincreased viral load. Individual HLA-specific polymorphisms wereselected for examination. A polymorphism at an anchor residue wasconsidered. HLA-A11 associated K166x is at the anchor position of anHLA-A11 epitope RT(158-166 LAI) and HLA-A11 groups with and without thepolymorphism had sufficient numbers for comparison. To exclude effectsof antiretroviral therapy, only patients with HIV-1 RT sequence andviral load results prior to treatment were analyzed. The closestpre-treatment viral load measurement taken after the HIV-1 RTsequencing, was compared between all groups. In HLA-A11 individuals(n=19), the median pre-treatment viral load was 5.54±0.46 log cps/mLplasma (median±SD) in those with K166x (n=4) compared with 4.31±0.82 logcps/mL, in those without K166x (n=15, P=0.045, Wilcoxon). Median viralload in HLA-A11 individuals without K166x was not significantlydifferent from that of all non-HLA-A11 individuals (data not shown).

A second putative CTL escape mutation within a CTL epitope but not at aprimary anchor position showed a similar effect. The medianpre-treatment viral load in HLA-B7 patients with S162x (n=18) wassignificantly higher (5.41±1.04 log cps/mL) than in those without S162x(n=15, 4.57±0.83 log cps/mL, P=0.046, Wilcoxon). For both HLA-A11 andHLA-B7 groups, the mean CD4 T cell count and percentage of individualswith AIDS at baseline was not significantly different between those withand those without these putative CTL escape mutations.

A global analysis of factors influencing viral load at a populationlevel was then conducted. A Cox proportional hazards model was carriedout in which pre-treatment viral load was the outcome and all HLAalleles and HLA-specific polymorphisms were discrete covariates. WhenHLA alleles and polymorphisms were included as interaction terms (i.e. apolymorphism and it's positively associated HLA allele, or consensusamino acid and the negatively associated HLA allele) the overallsignificance value of the model improved. The former model had a loglikelihood of −32.0765 with 40 degrees of freedom and the latter modelhad a log likelihood of −15.4165 with 25 degrees of freedom. Theimprovement in the model was calculated using a chi square distributionwith a value of two times the difference in log likelihood values withdegrees of freedom (33.32˜χ(15), giving a P-value of 0.004). Thissuggested that the presence in individuals of viral CTL escape mutationsas putatively identified in these analyses, explained the viral loadvariability in the population to a greater extent than either HLAalleles or viral polymorphisms per se.

Logistic regression models of polymorphism incorporating HLA-DRB1 broadalleles as covariates along with HLA-A and -B alleles and polymorphismsat other positions were repeated. Only patients in the cohort with DRB1alleles defined by DNA sequence based typing were included in thisanalysis (n=294). There were 13 sites of polymorphism between positions20 and 227 that were significantly associated with HLA-DRB1 alleles.Only five T helper cell epitopes have been mapped within this segment ofHIV-1 RT (A. S. de Groot et al., J of Infectious Diseases 164, 1058-1065(1991); S. H. van der Burg et al., J Immunol 162, 152-160 (1999); F.Manca et al., J of Acq. Imm. Def Syn. & Hum. R 9, 227-237 (1995); F.Manca et al., Eur J Immunol 25, 1217-1223 (1995)) and only one,RT(171-190), has been assigned HLA-DRB1 allele(s) specificity (S. H. vander Burg et al., J Immunol 162, 152-160 (1999)). Four of the five knownCD4 T helper cell epitopes encompassed sites of HLA-DRB1 allele-specificpolymorphism found in the models described herein. These analyses didnot detect an HLA-DRB1 association within RT(171-190). There were 10HLA-DRB1 associated polymorphisms that were not within known T helpercell epitopes.

According to these analyses, HIV-1 RT sequence is relatively conservedamong isolates however, even in a stable, geographically isolatedpopulation of HIV-1 infected persons there is sequence diversity ofHIV-1 RT. The population consensus sequence was used in this study asthe presumptive wild-type sequence best adapted to the population as awhole and was almost identical to the clade B reference sequenceHXB2-RT. Yet, within the study population, variation from this consensussequence was evident even in a segment of HIV-1 RT. Findings presentedherein suggest that this diversity is the net result of at least twocompeting evolutionary pressures selecting for or against change at eachamino acid. Foremost is the need to maintain functional integrity of thevirus. Within the bounds of this fundamental constraint, a strongpredictor of viral polymorphism appears to be host HLA.

There were 64, often clustered, polymorphisms in HIV-1 RT associatedwith specific HLA-A or HLA-B alleles. Polymorphisms occurred at sitesthat were within or proximate to published CTL epitopes, and correlatedwith the HLA alleles to which these epitopes are known to be restricted.This correlation was itself highly statistically significant and severalassociations still remained significant after rigorous correction formultiple comparisons across the whole analysis. The detailed features ofspecific examples, such as HLA-B*5101 associated I135x, were highlysuggestive of CTL escape mutation affecting HLA-peptide binding.Polymorphisms at non-primary anchor residues of CTL epitopes, such asHLA-B*3501 associated D177x, HLA-B7 associated S162x and others mayconfer a survival advantage to the virus by disrupting T cellreceptor-peptide recognition, epitope processing from precursor proteinor by inducing antagonistic CTL responses. The five HLA-specificpolymorphisms at residues flanking CTL epitopes may indicate viralescape by disruption of proteosome peptide cleavage. This form of escapehas been particularly difficult to identify by standard techniques thatuse only the epitope peptide to measure CTL responses. HLA-specificpolymorphisms increased over time, were associated with secondarychanges at other positions and were predictive of viral load at apopulation level. The effect of single residue changes on viral load isespecially striking given that there may be a polyclonal immune responseagainst epitopes in other HIV-1 genes and other independent influenceson viral load such as CCR5 polymorphism. Taken together, these datasuggest that the HLA-specific polymorphisms identified herein in HIV-1RT represent the net effects of in-vivo CTL escape mutation inindividuals. By implication, those polymorphisms not within publishedCTL epitopes may indicate where new or putative CTL epitopes arelocated. The HLA associations that are very strong (with high OR), andwhich are clustered or remain significant after correction for multiplecomparisons are those most likely to represent viral escape mutations inCTL epitopes that are yet to be defined.

CTL escape mutation has been well characterized in individuals withHLA-B8 (most commonly), HLA-B44, HLA-B27, HLA-AL 1 and HLA-A3, who mayhave been more escape-prone because of narrow range, oligoclonal CTLresponses. These data suggest that CTL escape mutation is common andwidespread, selected by responses restricted to a much wider range ofHLA alleles than has been studied in individual cases. Though manyHLA-specific polymorphisms increased over time in this study, some werepresent in first pre-treatment HIV-1 RT sequence and could reflect viralfounder effects, have been variants selected at transmission or duringthe early CTL response of acute infection. The single HLA-B*5101 patientwithout I135x was distinguished by use of HAART in acute infectionwhilst highly viremic. This patient presented in the first days ofinfection with no symptoms, suggesting he had not yet mounted a CTLresponse. Presumably, the immune selection pressure was reduced oreliminated, arguing that I135x is selected during the acute CTLresponse, rather than selected at transmission or in chronic infectionin HLA-B*5101 individuals. Protection from CTL escape variants maycontribute to the effect of HAART in acute HIV infection leading tostronger chronic inhibitory CTL responses which, to date, has beenlargely attributed to preservation of HIV-1 specific CD4 T cell help.

HLA alleles were also associated with lack of polymorphism at certainresidues, including at residues without functional constraint and theseassociations contributed independently in a comprehensive model of viralload. Unlike positive immune selection causing demonstrable escape overtime in individuals, negative immune selection favors preservation ofwild-type virus in vivo and so could only be evident at a populationlevel. It is possible that consensus or wild-type virus is primordiallyadapted to the CTL responses that have most often been encountered (thatis, those restricted to the most common or evolutionary conserved HLAalleles in the host population). For HIV-1, this may account, at leastin part, for HIV-1 clade differences. Population adaptation could alsoexplain why selection of escape polymorphisms in CTL epitopes restrictedto the common allele HLA-A*0201 was not demonstrated in studies thathave argued against an important role for immune escape and even whysurprisingly few HLA-A2 and HLA-A1 restricted epitopes have been mappedin HIV-1. Furthermore, studies of HIV-1 exposed seronegative individualssuggest that CTL responses can alter viral infectivity andsusceptibility to established primary HIV-1 infection. The HLA class Ialleles associated with natural HIV-1 resistance or susceptibilityappear to differ between racially distinct populations. To some extentthis may reflect differences in the HLA alleles that are common indifferent populations and the degree to which a ‘population-adapted’consensus virus can adapt to the individual.

Demonstration of 13 HLA-DRB1 specific polymorphisms in HIV-1-RT(adjusted for HLA-A and HLA-B associations and secondary polymorphisms)lend support to the possibility of CD4 T helper escape mutation in humanHIV-1 infection. Relatively few T helper cell epitopes in HIV-1 RT arepublished and their HLA-class II restrictions are not defined, so it isdifficult to assess whether these results are consistent with T helperselection of escape mutation. However, HLA class II restricted CD4 Thelper responses have a central role in HIV-1 control and there areseveral reported associations between HLA class II alleles and HIVdisease susceptibility and progression including after HAART.

The population-based approaches in this study reveal how both positiveand negative selection forces compete at single residues to driveprimordial and current viral evolution in vivo. These results areespecially notable considering the factors that reduce the likelihood ofobserving significant HLA associations in such analyses. First, thepower to detect associations is not constant for all HLA allele/viralresidue combinations. Large numbers of individuals would be needed toobserve any polymorphism at residues under immune pressure to mutate butwith strong functional constraint, or any associations with HLA allelesthat are rare. The use of formal power calculations identifies those HLAassociations that cannot be excluded and would need larger data sets tobe examined. Second, the molecular subtype of an HLA allele predicts itsbinding properties in vivo, as shown by the enhancement of associationsbetween HLA-B5 and I135x, and HLA-B35 and D177x by high resolution HLAtyping. Other alleles with multiple splits of similar frequency (e.g.HLA-A10 or HLA-A19) may have had associations that were not detectedbecause only broad alleles were considered. Furthermore, molecularsplits that have opposing effects at the same viral residue would negateany association with the broad allele. Finally, published epitopes aremore likely to be in conserved regions, as studies tend to uselaboratory reference strains as target antigens and conserved regionsare more likely to have measurable immune responses in vivo. Thisapproach, in contrast, preferentially detects putative immune epitopesin variable regions, making it complementary to standard epitope mappingmethods. Insufficient patient numbers, lack of molecular based HLAtyping and lack of known epitopes in conserved regions could all accountfor the immune epitopes in which ‘expected’ HLA-specific polymorphismswere not detected, and could mean that the strength (OR) of thedemonstrated associations were underestimated in some cases.

The generation of chance associations as a result of comparisons madewith multiple covariates (HLA alleles) and at multiple residuespotentially hampers such analyses, though power calculations and otherscreening procedures considerably restrict the number of alleles andpositions that are examined. The degree to which P-values generatedwithin multivariate logistic regression models are corrected for thenumber of residues examined will then depend on the size of the gene(s)that has been arbitrarily chosen for study. With such correction, theapproach will lose power to detect associations in direct proportion tothe size of the gene region selected, decreasing false positiveassociations (higher specificity) but at the cost of losing truepositive associations (lower sensitivity). These analyses of HIV-1 RTprovided a gradation of P-values uncorrected for multiple comparisons,reflecting a gradation in strength of associations. Independentbiological validation, rather than statistical means, will bestdetermine what p-value cut-offs are optimal for either sensitivity orspecificity. If correction is to be made (for high specificity) therandomization procedure undertaken allows the number of effectiveindependent comparisons in the entire analysis to be estimated. ThoseHLA associations with P-values that withstand this rigorous correctionhave been highlighted by these methods. These highly robust associationsrepresent the starting point to map new epitopes in HIV-1 RT.

In terms of the known associations between certain HLA and HIV-1 diseaseprogression, HLA allele frequencies influence adaptation of ‘wildtype’HIV-1 at a population level. However, in-vivo evolution proceeds withinindividuals of diverse HLA. This analysis shows that it is the presenceof HLA alleles with their corresponding HLA-specific viral polymorphisms(or consensus) that is more predictive of viral load than the HLAalleles alone. It has also been suggested that it is the breadth of CTLresponses that determines the risk of viral escape and hence, clinicalprogression. Narrow monospecific responses, as seen in HLA-B*5701 longterm non-progressors, can be protective but may also increase risk ofviral escape in individuals with the deleterious HLA allele, HLA-B8.Increasing heterozygosity of the three HLA class I loci, which wouldpredict broader polyclonal responses, has been shown to predict slowprogression to AIDS. Successful viral CTL escape mutation depends onhaving low functional barriers to mutation at the appropriate residues,so it may be the balance struck between the breadth of hostepitope-specific CTL responses and viral functional constraint at thoseepitopes that is important. Hence narrow CTL responses could beprotective if directed against conserved epitopes, but not protective orharmful if directed against epitopes susceptible to variation. Theability to map both the range of putative epitopes and the observedpolymorphism of the epitope in a population for many HLA alleles at onceis thus very useful. Future analyses of HIV-1 RT should also incorporatereverse transcriptase inhibitors as covariates in the models to examinethe interaction between drug-induced primary or compensatory mutationand HLA-associated primary or secondary polymorphism. If immunepressures and antiretroviral drugs compete at sites within viralsequence, a greater or lesser tendency to drug resistance and responsemay be seen in patients depending on their HLA genotype.

Individualization of antiretroviral therapy may be improved ifsynergistic or antagonistic interactions between immune pressure anddrug pressure are better understood. Just as these methods haveidentified the location of putative immune epitopes in HIV-1 RT,candidate epitopes in other HIV-1 proteins or proteins from othermicroorganisms could be screened for in the same way and then confirmedusing standard assays of epitope-specific immune responses in vitro orin vivo. In HIV envelope, effects associated with anti-HIV antibodyresponses, CCR5 and CXCR4 genotype and any other polymorphisms of genesencoding products targeting envelope proteins can also be considered.

In other studies, HIV-1 protease was examined using the methodsdescribed above. In particular the method examined whether, in bothHIV-1 RT and protease, host CTL pressure and drug pressure may competeor synergize at specific sites, which then influence drug resistancepathways in ways unique to the individual of given HLA type.

Bulk HIV-1 RT and protease pro-viral DNA sequences obtained from 550individuals with HIV-1 infection were analyzed. Single amino acidpositions were examined at a time. The consensus amino acid for eachposition was determined and compared against the amino acids present ineach individual's autologous viral sequence at the correspondingposition. A multivariate analysis for a single residue (for example,residue 184 of HIV-1 RT, methionine in consensus) was carried out inwhich the outcome of interest was the presence or absence of a specifiedpolymorphism (M184V) or alternatively, any variation from consensus(M184x). The statistical significance of association(s) between thisoutcome and covariates such as the antiretroviral drugs used by theindividuals and/or their HLA types, were then determined. Using modelselection steps as previously described, this process was repeated forevery residue making up the fill HIV-1 RT and protease proteins.

The study population was drawn from The Western Australian (WA) HIVCohort Study. Start and stop dates of all antiretroviral treatments arerecorded. HLA-A and HLA-B genotyping has been routinely performed atfirst presentation since 1983. HIV-1 RT proviral DNA sequencing has beenrequested at first presentation (prior to treatment where possible) andduring routine clinical management of antiretroviral therapy since 1995.HIV-1 protease sequencing was commenced in 1997. The total cohort inthis study comprised 550 individuals. All had at least one HIV-1 RTsequence recorded and 419 individuals had protease sequence availablefor analysis.

All analyses were performed as described above. The population consensussequence for HIV-1 RT(20-227) and protease (1-99), with standard HXB2numbering and alignment, was used as the reference sequence in allanalyses. The population consensus sequence matched the clade Breference sequence HIV-1 HXB2 at all positions in HIV-1 RT except 122(lysine instead of glutamate) and 214 (phenylalanine instead ofleucine). In HIV-1 protease, consensus sequence differed at position 37(asparagine instead of serine) and 63 (proline instead of lysine).

Power calculations were conducted to limit analyses to only thosepositions, drugs and HLA alleles for which there was at least 30% powerto detect associations with OR>2 (positive associations) or <0.5(negative associations) with p-value<0.05. Individual covariates werethen assessed for univariate association with mutation/substitution, anddiscarded if p-values were >0.1 and then subjected to forward selectionand backwards elimination procedures. Exact p-values were determined foreach association. Finally, a randomization or bootstrapping procedurewas carried out to determine a correction factor for final (HLA)associations to adjust for multiple comparisons.

HIV-1 DNA was extracted from buffy coats (QIAMP DNA blood mini kit;Qiagen, Hilden, Germany) and codons 20 to 227 of RT were amplified bypolymerase chain reaction. A nested second round PCR was done and thePCR product was purified with Bresatec purification columns andsequenced in both forward and reverse directions with a 373 ABI DNASequencer. Raw sequence was manually edited using software packagesFactura and MT Navigator (PE Biosystems).

Only well characterized drug resistance mutations were selected for thisexamination. Among the 273 individuals in the cohort with pre-treatmentHIV-1 RT sequences available, 12 (4.4%) contained HIV-1 RT primaryand/or secondary mutations resistance mutations. Of 168 individuals withpre-treatment protease sequences available, 49 (29.2%) had proteaseprimary resistance mutations. For those individuals with knownseroconversion date (n=182), the mean time from seroconversion to timeof first pre-treatment sequence was 5.7 years.

The pooled sequences of the whole cohort were then examined. 288 (52.4%)of these individuals had either past or current treatment withantiretroviral drugs, including NRTIs in 52.0%, NNRTIs in 8.2% and PIsin 16.4%. For each logistic regression model carried out for oneposition at a time, only the specific amino acid substitutioncharacteristic of drug resistance was considered as the outcome. Allsequential sequences for each individual were analyzed, spanning a meanperiod of 1.9 years per person. The earliest presence of a resistancemutation was recorded as a positive outcome, all subsequent sequenceswere discarded and all drug exposures prior to the outcome were enteredas covariates. The outcome was recorded as negative if mutation had notdeveloped in any sequence.

Primary and/or secondary drug resistance mutations were detected in33.6% of subjects in post treatment HIV-1 RT sequences. The mutationsdetected with sufficient frequency to be examined in the logisticregression analyses included M41L, D67N, K70R, L74V, K103N, Y181C/I,M184V, G190A/S, L210W, T215Y and K219Q/E, whilst K65R, 75, V108I, Q151Mand P225H were only rarely or not detected (<4.0% of sequences) andtherefore had little power to be examined. For all the resistancemutations examined, the drug(s) associated with selection of themutation at a population level corresponded to those known to select forthe mutation from other studies (Table 2). For example, use oflamivudine was associated with the development of M184V with an OR of 19(p<0.001). Use of zalcitabine independently increased risk of developingM184V (OR=3, p=0.004). Positive associations between L74V or M184V anduse of abacavir were not detected in the study population. There wasinadequate statistical power to detect associations between use ofdelavirdine and mutations as this agent was rarely used. Table 2—Theamino acid substitutions in HIV-1 RT examined in models, with theirpublished causative antiretroviral agent(s) and those associated withthese substitutions at a population level in this study. OR-odds ratio,ZDV-zidovudine, ddI-didanosine, 3TC-lamivudine, d4T-stavudine,ABC-abacavir, NRTI-nucleoside analogue reverse transcriptase inhibitor,NNRTI-non-nucleoside analogue reverse transcriptase inhibitor.

Amino acid substitutions Published Drug association(s) examined inprimary drug detected at a population HIV-1 RT association(s) level instudy cohort OR P-value M41L thymidine NRTI ZDV 3 <0.001 D67N ZDV? ZDV10 <0.001 K70R thymidine NRTI ZDV 2 <0.001 L74V ddI ddI 8 <0.001 ABCK103N NNRTI nevirapine 6 <0.001 efavirenz 6 <0.001 Y181C/I nevirapinenevirapine 9 <0.001 delavirdine M184V 3TC 3TC 19 <0.001 ddC ddC 3 0.004ABC G190A/S nevirapine nevirapine 11 <0.001 L210W ZDV ZDV 2 0.016 T215 Ythymidine NRTI ZDV 4 <0.001 K219Q/E ZDV ZDV 4 <0.001

There were primary protease inhibitor (PI) resistance mutations (D30N,M461/L, G48V, V82A/T/F, L90M) detected in 24.1% and secondary PIresistance mutations (L10I, 154V/L, A71V/T, 73, V771, 184V, N88S) in30.3% of individuals with post-treatment protease sequencing. All buttwo (D30N and nelfinavir, G48V and saquinavir) of the expected theassociations between individual PIs and primary PI resistance mutationswere evident in the study population (Table 3). There was inadequatestatistical power to detect associations between use of amprenavir orlopinavir and mutations.

The models as described above were repeated for all amino acids in HIV-1RT and protease and added the HLA-A and -B (broad) serotypes of allindividuals as covariates, along with drug exposures. At those positionsthat were known primary or secondary drug resistance mutation sites, thecharacteristic drug resistance amino acid substitution was specified asthe outcome. At all other positions, any non-consensus amino acid wasthe outcome.

TABLE 3 Amino acid substitutions in HIV-1 protease examined. Amino acidsubstitutions Published Drug association(s) examined in primary drugdetected in study HIV-1 protease association(s) cohort OR P-value L10I/Rsecondary broad indinavir 2 0.005 PI saquinavir 3 <0.001 D30N nelfinavirND M46I/L primary indinavir 3 0.006 indinavir G48V primary ND saquinavirI54V/L indinavir indinavir 5 <0.001 A71V/T secondary broad indinavir 20.017 PI saquinavir 3 <0.001 73 secondary broad indinavir 4 0.002 PIsaquinavir 10 <0.001 V77I secondary broad indinavir 2 0.026 PI V82A/T/FIndinavir indinavir 3 0.01 ritonavir ritonavir 2 0.03 I84V Indinavirindinavir 6 <0.001 N88S Nelfinavir nelfinavir 11 <0.001 L90M Saquinavirsaquinavir 2 0.012 nelfinavir nelfinavir 9 <0.001 PI = proteaseinhibitor

TABLE 4 Characteristic HLA-specific amino acid substitutions in HIV-1 RTfor those HLA alleles with strongest associations in models.%-percentage of individuals of HLA type that have the substitution intheir viral sequence. Site(s) of allele CTL epitope Most commonassociated (if known) amino acid HLA polymorphism containing/flankingsubstitu- allele in HIV-1 RT polymorphism tion(s) (%) A2 39 32-41 T39A11 53 E53 166 158-166 LAI K166 L. Menendez-Arias, A. Mas, E. Domingo,Viral Immunol 11, 167-181 (1998). Q. J. Zhang, R. Gavioli, G. Klein, M.G. Masucci, Proc Natl. Acad. Sci U.S.A 90, 2217-2221 (1993). L. Wagneret al., Nature 391, 908-911 (1998). S. C. Threlkeld et al., J Immunol159, 1648-1657 (1997). A28 32 K32 B5 135 128-135 IIIB I135T/V L.Menendez-Arias, A. reduced HLA Mas, E. Domingo, Viral binding in-Immunol 11, 167-181 vitro shown (1998). N. V. Sipsas et al., J ClinInvest 99, 752-762 (1997). H. Tomiyama et al., Hum Immunol 60, 177-186(1999). B7 158 156-165 A158 165 C. M. Hay et al., J Virol 73, T165 1695509-5519 (1999). L. E169 Menendez-Arias, A. Mas, E. Domingo, ViralImmunol 11, 167-181 (1998). C. Brander and B. D. Walker, in HIVmolecular immunology data- base, B. T. M. Korber et al., Eds. NewMexico, 1997). B8 32 20-26 K32 B12 203 203-212 E203 211 (HLA-B44) R211B15 207 Q207 B17 214 F214 B18 68 S68 135 I135 138 E138 142 I142 B35 121118-127 D121 177 175-185 D177 H. Shiga et al., AIDS, 10, 1075-1083(1996). B37 200 T200 B40 197 192-201 Q197 (HLA-B60) 207 207-216 Q207(HLA-B60)

All of the 63 polymorphisms positively (OR>1) associated with specificHLA-A or HLA-B allele(s) in these models (p≦0.05 in all cases) wereplotted on a map of HIV-1 RT in relation to the overall rate ofpolymorphism at each residue and known CTL epitopes. For 16 of theseHLA-specific polymorphisms associations, the polymorphisms were locatedwithin or flanking CTL epitopes with corresponding HLA restriction, inkeeping with CTL escape mutation and there appeared to be clustering of14 associations along the sequence. HLA-associated polymorphisms wereevident at four primary and nine non-primary anchor positions within theCTL epitopes and three were flanking CTL epitopes with corresponding HLArestriction. The characteristic amino acid substitutions present inthose with the HLA alleles that had the strongest associations were thendetermined (Table 4). There were 32 negative HLA associations (OR<1)also evident—indicating that polymorphism, or change away from consensuswas significantly less likely in the presence of these HLA allelesversus all others.

There were 48 HLA allele-specific polymorphisms in HIV-1 proteasedetected by the models. There were clustered polymorphisms for 8 HLAalleles, including those associated with HLA-B5 at positions 12, 13, 14and 16. There were HLA associated polymorphisms within and flanking theonly two published CTL epitopes, though none corresponded to thepredicted HLA restriction of the epitopes (based on binding motifs). Thestrongest HLA associations and their characteristic amino acidsubstitutions present in the cohort are shown in Table 5. There were 23negative HLA associations detected.

TABLE 5 Characteristic HLA-specific amino acid substitutions in HIV-1protease for those HLA alleles with strongest associations in models.Site(s) of allele associated Polymorphism in HIV-1 Most common aminoacid HLA allele protease substitution (%) B5 12 S (19.7%) B7 10 I(16.2%) B12 35 D (67.5%) 37 S (27.9%) B13 62 V (9.5%) B15 46 I (7.5%) 90M (8.0%) 93 L (51.6%) B37 35 D (54.6%) 37 D (57.3%) B40 13 V (22.4%)

There were four antiretroviral drug resistance mutations in HIV-1 RT(M41 L, K70R, T210W and T215 Y/F) and seven in protease (L10I/R, M46I/L,A71V/T, 73, V771, V82A/T/F and L90M) at which HLA alleles independentlyincreased the probability of the mutation. For example, the odds ofdeveloping M41L were markedly increased in individuals carrying HLA-A28compared with all other HLA-A or -B alleles (OR=41, p<0.001). To examinethis observation in more detail, we analyzed all individuals in thetotal cohort who had zidovudine exposure and HIV-1 RT sequencing at anytime after treatment (n=265). The prevalence of HLA-A28 in this set ofindividuals (8.0%) was comparable to that of the total cohort (8.3%).However, the HLA-A28 allele was over-represented in the 58 zidovudinetreated individuals with M41L (12.1%) compared with those 207individuals who did not develop this substitution (7.7%, RR=1.69,p=0.30, Fisher's exact test). A similar analysis was carried out on allindividuals who had nelfinavir treatment and HIV-1 protease sequencing(n=133). The presence of HLA-B13, associated with L90M in the logisticregression model (OR=13, p<0.001), was present in 40.0% of individualswith L90M compared with 18.7% without L90M after taking nelfinavir(RR=2.96, p=0.12, Fisher's exact test).

HLA alleles reduced the odds of two primary RT inhibitor resistancepolymorphisms, K103N (HLA-A19, 1/OR=4, p=0.04) and M184V (HLA-B16,1/OR=4, p=0.03) and one secondary PI resistance mutation L10I/R/V(HLA-A10, 1/OR=4, p=0.024), raising the possibility of antagonisticselection pressures in individuals with these specific HLA allelestreated with drugs that induce these mutations.

The findings of this study support a highly dynamic, host-specific modelof HIV-1 adaptation in-vivo, in which host CTL responses andantiretroviral therapy act as continuous, competing or parallelinteracting evolutionary forces at the level of single viral residues.

The distribution of common, known drug resistance mutations in the studycohort were comparable to that found in other large and smallobservational studies, including those in drug naïve individuals. Almostall known primary and most secondary drug resistance mutations wereevident as drug-associated polymorphisms across the population and inall these cases, the drug association corresponded to the knowncausative antiretroviral agents. The expected associations between D30Nand nelfinavir and G48V and saquinavir were not detected, though therewas (at least 30%) power to detect significant drug associations withOR>2 for both mutations. Notably, G48V has been reported most frequentlyin-vivo in patients taking high dose saquinavir monotherapy, which hasalmost never been used in this study cohort. In most cases, saquinavirhas been used together with ritonavir. Failure to detect knowndrug-associated polymorphisms using a population-based approach may bedue to a lack of statistical power if use of the drug or virologicalfailure on the drug is rare in the population, or if the mutation ispredominantly selected in-vitro but not in-vivo. This method may proveuseful for future novel antiretroviral drugs as a systematic way tocharacterize the most frequent, in-vivo drug resistance mutationsinduced by the drugs, even if the putative resistance sites in-vitro arenot known.

In the same models that confirmed the expected selection effects ofantiretroviral drugs, sequence diversity of several viral residuesacross the population was substantially influenced by the HLAcharacteristics of individual hosts. Previously, several HLAallele-specific polymorphisms in HIV-1 RT have been shown to correspondto known or likely sites of CTL escape, be more specific for fine HLAsubtypes compared with broad serotypes, increase in frequency over timeand predict higher plasma viral load. The models of HIV-1 RT sequencediversity have been further refined in this study by the adjustment fordrug induced changes, leaving a core set of 22 polymorphisms that wepresent as putative CTL escape mutations (Table 4).

Protease (RPLVTIKI; positions 8 to 15) is a predicted CTL epitope basedon the HLA-B5 binding motif and we found strong associations betweenHLA-B5 and a cluster of polymorphisms at positions 12, 13, 14 and 16.The considerable natural polymorphism of the protease gene has beennoted in several studies and it is possible that at least some of thisis CTL-driven (Table 5). The selected polymorphisms in HIV-1 RT andprotease shown in Tables 4 and 5 had one or all of the following keycharacteristics; their statistical association with a HLA allele wasvery strong and remained significant (p<0.05) after adjustment for drugassociated changes, polymorphisms at other positions (i.e. possiblesecondary mutations) and/or multiple comparisons, they fell within knownCTL epitopes with a corresponding HLA restriction or were clustered withother polymorphisms associated with the same HLA allele. In all cases,there was either one or two predominant amino acid substitution(s) inthe individuals carrying the HLA allele and the allele-associatedpolymorphism, as would be expected for a functional mutation selected bythe CTL response. In the case of I135TN, this substitution has beenshown by others to abrogate HLA binding to the viral epitope in-vitro.Thus, just as drug resistance mutations are considered ‘characteristic’or signatures of exposure to a particular antiretroviral drug, theseamino acid substitutions were characteristic for particular HLA alleles,and were evident in drug treated individuals.

Potent antiretroviral therapy with sustained suppression of HIV-1replication has been shown to coincide with a diminution of anti-HIV CTLresponses, suggesting that CTL escape is less likely to occur. Thestudies that have documented CTL escape to fixation over time inindividuals have all been in the untreated. In this study cohort,individuals were more likely to have HIV-1 RT and/or protease sequencingperformed during virological failure, rather than when successfullyvirologically controlled. Though we cannot determine the time at whicheach HLA-specific polymorphism typically first appears, thedemonstration of independent HLA and drug associated effects on viralsequence implies that CTL may still exert selection pressure during orafter a period of antiretroviral drug therapy in some individuals.

There are a few viral residues where CTL pressure and drug pressureappeared to compete or concur in driving to either change or not changefrom the wildtype amino acid. This raises the intriguing possibilitythat anti-HIV CTL responses could be an explanation for discordance ofin-vitro/in-vivo drug resistance patterns, discordance of genotypic andphenotypic resistance and variable rates of emergence of drug resistancemutations in different individuals. Interactions between CTL pressureand drug pressure are therefore germane to many aspects of contemporarytreatment strategy, such as comparisons of different antiretroviralregimens, structured treatment interruptions (STIs) and different timingof treatment initiation. It is increasingly acknowledged that the designand interpretation of studies on these issues is limited by anincomplete understanding of what determines biological variability indisease between individuals. Our findings to date argue for HLA typingand viral genotyping to inform the design of future clinical studies.For example, STIs would not be expected to enhance HIV specific CTLresponses in individuals who have already escaped from those responsesin-vivo. Being able to prospectively identify individuals with orwithout the key escape mutations for their HLA, would enable STIs to beadministered to those most likely to benefit from them. Similarly,studies of individualized drug choice and treatment timing could beinformed by this data. In the same way that baseline and periodicpost-treatment RT and protease resistance genotyping has now become thestandard of care for optimization of drug treatment, viral genotypingfor critical escape mutations may greatly enhance individualization ofantiretroviral treatment in the future.

Other groups have independently reported a number of these epitopes,e.g. an HLA-A11 restricted CTL epitope has been described betweenpositions 117 and 126 of HIV reverse transcriptase (B. Sriwanthana etal., Hum Retroviruses 17, 719-34 (2001)). The following associationswere also identified within subsequently published CTL epitopes: HLA-A3at 101 within an HLA-A3 restricted CTL epitope RT(93-101; C. Brander andP. Goulder, in HIV Molecular Immunology Database. B. T. M. Korber etal., Eds. New Mexico, 2001); HLA-A19(30) at 178 within an HLA-A*3002epitope (173-181; C. Brander and P. Goulder, in HIV Molecular ImmunologyDatabase. B. T. M. Korber et al., Eds. New Mexico, 2001; and P. Goulderet al., J. Virol 75(3), 1339-47 (2001)) and HLA-B40 at 207 within anHLA-B*4001 restricted CTL epitope (202-210; C. Brander and P. Goulder,in HIV Molecular Immunology Database. B. T. M. Korber et al., Eds. NewMexico, 2001).

HIV and ancestral retroviruses have evolved under intense selectivepressure from HLA (or MHC) restricted immune responses. HIV has highlydynamic and error prone replication and evidence of this HLA restrictedselective pressure can be seen in individual patients and at apopulation level. Of 473 Western Australian patients studied, no twopatients had the same HIV Reverse Transcriptase amino acid sequence.Polymorphisms were most evident at sites of least functional orstructural constraint and frequently were associated with particularhost HLA Class I alleles. Patients who had escape mutations at theseHLA-associated viral polymorphisms had a higher HIV viral load. Thisinformation indicates which HIV peptides (epitopes) stimulate thestrongest protective immune response against the virus after infection.Those same epitopes should afford the strongest protection if given in avaccine before exposure to the virus.

The protection afforded by a preventative HIV vaccine will depend on thebreadth and strength of the HLA restricted immune responses elicited bythe therapeutic and the extent to which the infecting HIV sequence hasescaped those responses. The objective is (1) for the therapeutic toinduce the maximum number and strength of HLA-restricted CTL responsesand (2) to have the maximum number of identical matches betweentherapeutic epitopes and incoming viral epitopes (or for the viralepitopes to at least be similar enough to the therapeutic epitope tostill be recognized by the therapeutic induced CTL response).

The traditional approach has been to try to include conservedepitopes—stretches of viral proteins that are eight to 12 amino acidslong that are invariably present in all HIV variants. However, studiespresented herein indicate that the virus and its ancestors have evolvedunder intense selective pressure from HLA-restricted immune responsesand therefore tend not to have conserved epitopes recognized by commonHLA types.

A preliminary analysis of the first 80 patients with full-lengthsequencing has revealed HLA specific associations in all the proteinsand escape at these residues correlated with a higher pre-treatmentviral load. The strongest associations and their relationship to HIVviral load are shown in Table 6. FIG. 12 shows the relationship betweenthe degree of viral adaptation to HLA-restricted responses and the viralload. The number and strength of HLA-restricted associations and thedegree to which these explain the variability in pre-treatment viralload will increase as data on a larger number of patients becomesavailable.

TABLE 6 Estimated Amino acid change in Consensus Non-escaped Proteinposition HLA Odds ratio P-value viral load amino acid amino acidIntegrase 11 B*4402 166.02 <0.0001 1.39 Glutamate Aspartate Nef 14C*0701 6.78 0.0001 0.31 Proline Serine p6 34 A*2402 52.59 0.0002 −0.02Glutamate Aspartate Nef 71 B*0702 19.40 0.0002 0.28 Arginine Lysine p625 B*4402 66.34 0.0003 0.91 Serine Proline Integrase 119 DRB1-0101429.45 0.0004 −1.10 Serine Arginine Vpr 84 DRB1-0701 0.03 0.0005 −0.45Threonine Isoleucine Integrase 122 C*0501 17.24 0.0005 0.63 ThreonineIsoleucine Integrase 119 DRB1-0701 144.67 0.0005 −0.12 Serine GlycineProtease 37 DRB1-1302 19.98 0.0006 0.23 Asparagine Serine Integrase 17B*4001 8.00 0.0008 −0.31 Serine Asparagine p6 29 A*2402 9.38 0.0008 0.43Glutamate Glycine Integrase 119 B*4402 273.63 0.0009 0.53 Serine Prolinep7 9 B*1801 30.54 0.0010 0.20 Glutamine Proline

A simulation was undertaken to determine the likely efficacy ofdifferent preventative vaccine candidates assuming an HIV negativetarget population with the same HLA diversity as the HIV positiveWestern Australian cohort was exposed to the same range of viraldiversity observed in the Western Australian HIV positive cohort. Inother words a hypothetical population of 249 HIV negative patients withthe identical HLA types as the 249 HIV positive Western Australianpatients was examined. The possibility of the first HIV negative patientbeing exposed to the virus sequenced in the first HIV infected patientwas considered, then the virus in the second HIV positive patient and soon until all 80 viral sequences had been considered. This process wasrepeated for the second hypothetical HIV negative patient and so onuntil all 249 HIV negative subjects had been considered.

In the first analysis (FIG. 12B), for each potential therapeuticcandidate, the number of beneficial amino acid residues that werepresent in the hypothetical therapeutic (i.e. a consensus at a positiveHLA association and a match between the therapeutic and the incomingvirus, or second most common residue at a negative HLA association and amatch between this second most common residue and the incoming virus)was calculated. In the second analysis (FIG. 13), an estimated strengthof the HLA-restricted immune response that would be induced by eachtherapeutic in response to each of the potential incoming viruses usingthe viral load results as illustrated in the estimated change in viralload column shown in Table 6 was calculated. Generally the use ofconsensus sequence for the study population reduced but did noteliminate the problem posed by the viral diversity and inclusion of themaximum number of HLA-A, B or C specific viral polymorphisms(particularly those associated with large viral load increases onescape) is predicted to improve HLA-restricted responses.

The following discussion provides an example of a hypothetical option toaddress HIV specific immune responses. At the commencement of treatmenta blood sample is taken from each patient for use in HIV sequencing andHLA typing to determine which residues and hence virus populations havealready escaped from HLA-restricted immune response using the HLA-viralpolymorphism associations derived from a population based analysis. Themethods for carrying out this analysis are described above.

Delivery of the vaccine to the patient is achieved using a fowlpoxvector (or any other vector suitable for deliver of a protein sequenceto a patient). This is achieved by well known and standard techniqueswhich include isolation of a nucleotide sequence that encodes theproteins that are used in the vaccine. The nucleotide sequence is theninserted into the vector (e.g., fowlpox) and then delivered to a patientat levels and in a manner that leads to protein expression within thepatient.

If the HIV sequence selected for use in the vaccine does not encode thespecific sequence mentioned that sequence may be modified using wellknown and well understood techniques in molecular biology (see Ausubel,F., Brent, R., Kingston, R. E., Moore, D. D., Seidman, J. G., Smith, J.A., Struhl, K. Current protocols in molecular biology. Greene PublishingAssociates/Wiley Intersciences, New York., the text of which isincorporated herein by reference) including site directed mutagenesistechniques as an example.

A hypothetical treatment using a vaccine to maintain HIV specific immuneresponses as HIV antigen wanes during effective highly activeantiretroviral therapy (HAART) can be administered according to thefollowing method. At the commencement of treatment a blood sample istaken from each patient for use in HIV sequencing and HLA typing todetermine which residues and hence virus populations have alreadyescaped from HLA-restricted immune response using the HLA-viralpolymorphism associations derived from a population based analysis. Thepatient is then placed on HAART to inhibit HIV replication decreasingthe availability of HIV antigen to sustain HIV antigen specific immuneresponses. The protocols of the HAART treatment used depend on thepatient to be treated. Physicians will adopt an appropriate protocolbased on the level of infection in a patient, the health of the patientetc.

Over the course of HAART, regular monitoring of viral loads is carriedout to measure the effect of treatment. Once viral load has wanedsufficiently the patient is then placed on a vaccination protocol aimedat the desired epitope. The constitution of the therapeutic may varydepending on the precise needs of the treating physician.

A hypothetical treatment using a vaccine to prevent or delay theemergence of anti-retroviral drug resistance mutations in patients onhighly active antiretroviral therapy is described below. Combinationantiretroviral therapy (ART) has resulted in a 60% reduction inmortality from HIV-1 and provided great hope for those infected. Howeverthe development of drug resistance is a major hurdle in the long-termbenefit it can provide both in the developed and developing world.Resistance to HIV medications following treatment is now common, withstudies in the USA and Ivory Coast demonstrating over 50% of treatedpatients harbouring some resistance to HIV.

Vaccination aims to prevent the onset of disease states and has providedincalculable benefit to entire communities and humanity as a whole. Therole of vaccination in those already infected with a particular diseaseis only currently being evaluated, especially in relation to HIV-1. Avaccine that could prevent or delay the development of drug resistancein those already infected with HIV-1 could provide significant benefitfor the millions of people living with this disease.

The clinical benefit of therapeutic vaccines in HIV infected patientshas been disappointing to date potentially because the patient hasalready been exposed to the vaccine antigens and the vaccines epitopesare to a variable extent escaped from HLA-restricted immune responses.Antiretroviral resistance mutations are detrimental to the patient butin this case the patient has not yet been exposed to the antigen. Use ofa sufficiently immunogenic vaccine such as the DNA/Fowlpox prime/boostvaccine should provide high level T cell immunogenicity.

The objective is for the therapeutic construct to match the new epitopecreated when the anti-retroviral drug resistance mutation emerges.Ideally the autologous virus in each patient would be sequenced and anidentical virus in all respects apart from the introduction ofcharacteristic drug mutations be used in the therapeutic construct (i.e.a vaccine individualized to each patient). According to thishypothetical example, the patient is vaccinated by a process ofintroducing one or more vectors into the patient, which are adapted toexpress the protein sequence of the vaccine.

The vaccine is delivered as follows. A fowlpox vector is constructedcontaining cDNA. Insertion of the cDNA sequence encoding the epitopesequence should be carried out in a manner to ensure that the sequenceswill be expressed when introduced into a patient. The vector may alsocontain all expression elements necessary to achieve the desiredtranscription of the sequences. Other beneficial characteristics canalso be contained within the vectors such as mechanisms for recovery ofthe nucleic acids in a different form. Reactions and manipulationsinvolving nucleic acid techniques can be performed as generallydescribed in Sambrook et al., 1989, Molecular Cloning: A LaboratoryManual, Cold Spring Harbor Laboratory Press, and methodology.

The constructed vector is then introduced into cells by any one of avariety of known methods within the art. Methods for transformation canbe found in Sambrook et al., Molecular Cloning: A Laboratory Manual,Cold Springs Harbor Laboratory, New York (1992), in Ausubel et al.,Current Protocols in Molecular Biology, John Wiley and Sons, Baltimore,Md. (1989), Chang et al., Somatic Gene Therapy, CRC Press, Ann Arbor,Mich. (1995), Vega et al., Gene Targeting, CRC Press, Ann Arbor, Mich.(1995) and Gilboa, et al. (1986) and include, for example, stable ortransient transfection, lipofection, electroporation and infection withrecombinant viral vectors.

Information concerning the extent to which the strain of HIV infectingan individual has escaped their HLA-restricted immune response may beused to individualize and guide the timing and type of treatment to beused. In general treatment should aim to prevent further HIV escape fromor adaptation to HLA-restricted immune responses.

The following is a hypothetical example of a diagnostic technique.Sequences identified as escape mutations are synthesized using standardprotein synthesis techniques known in the art. Such techniques aredescribed in Sambrook et al., Molecular Cloning: A Laboratory Manual,Second Edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor,New York (1989); Ausubel, F., Brent, R., Kingston, R. E., Moore, D. D.,Seidman, J. G., Smith, J. A., Struhl, K. Current protocols in molecularbiology. Greene Publishing Associates/Wiley Intersciences, New York.Once the proteins have been sequences they are used to generateantibodies according to, for instance, the methodology described firstin Kohler and Milstein, Nature, 256:495-497 (1975). Antibodies preparedby the above methodology can be employed in an ELISA assay as describedin Chapter 11 of Ausubel, et al.

FIG. 16 is a block diagram of one example of a system 1600 thatfacilitates making a prediction. The system 1600 comprises a machinelearning classifier 1610 to make predictions 1630. By way of example,the system 1600 can predict a pathogen characteristic relating to adisease state of the host, such as a disease state affecting the host'simmune system (e.g., an acquired immunodeficiency).

The machine learning classifier 1610 can be trained, for instance, on aplurality of associations 1620 between the host and the pathogen. Anysuitable type of association 1620 can be used to train the machinelearning classifier including but not limited to an MHC-type of anindividual and a mutation of a microbe. If a suitable association 1620exists, any pathogen characteristic can be predicted (e.g., apolypeptide, polynucleotide, etc.). By way of example, the system 1600can be trained using data relevant to a medical condition, for instance,an association 1620 between host alleles and HIV characteristics (or anyother pathogenic organism, e.g., HCV, HSV, etc.). The machine learningclassifier 1610 can be of any suitable type, such as a neural network,logistic regression, a decision tree, a support vector machine, etc. Themachine learning classifier 1610 can be encoded by computer-executableinstructions and stored on computer-readable media.

By way of example, the system 1600 can be employed to predict an epitopeof about 8 to about 11 amino acids in length. The classifier 1610 can belearned utilizing a plurality of associations 1620 between HLA-type andHIV escape mutations. Examples of a plurality of associations betweenHLA-type and HIV escape mutations are described supra in reference toFIGS. 8-13. To determine epitopes likely to be recognized by the immunesystem of an individual of a particular HLA type, the machine learningclassifier 1610 can be applied to each 8-11 amino-acid sequence in thevicinity of the mutation using, for instance, a 33-amino-acid-longwindow on either side of the mutation. The 33-amino-acid-long window ischosen because the positions flanking an epitope can influence whetherit is presented on a cell's surface. Using a 33-amino-acid-long windowallows for a 12-amino-acid-long flanking region on either side of a9-amino-acid-long epitope. Any window appropriate for the pathogencharacteristic and the association 1620 can be chosen.

By way of another example, logistic regression with features selected bythe wrapper method can be used to predict epitopes. Positive examplesinclude the 9-mers obtained from the LANL (hiv-web.lanl.gov) andSYFPEITHI (www.syfpeithi.de/) databases. Negative examples can begenerated at random from the marginal distribution of amino acids fromthe positive examples. The features used for prediction comprise: (1)the 2-4 digit HLA of the epitope; (2) the supertype of that HLA; (3) theamino acid at each position in the epitope; and (4) the chemicalproperties of each amino acid at each position in the epitope andconjunctions 1+3, 1+4, 2+3, and 2+4.

FIG. 17 shows a flow diagram illustrating one example of a method 1700of forecasting a portion of a target molecule anticipated to influencean organism's condition. The method can be encoded bycomputer-executable instructions stored on computer-readable media. Atstep 1710 of the method 1700, population data is employed toautomatically analyze one or more areas of the target molecule. At step1720, the portion of the target molecule expected to influence theorganism's condition is determined. Any of the techniques describedabove and below can be employed to accomplish steps 1710 and 1720.

FIG. 18 shows a flow diagram illustrating another exemplary method 1800of forecasting a portion of a target molecule. At step 1810 a classifieris learned according to population data. The population data, forinstance, can pertain to a relationship between a diverse trait of theparticular organisms and the target molecule (e.g., a relationshipbetween an allele and a sequence). At step 1820, the classifier isapplied to search the target molecule in the vicinity of a sitedetermined by the relationship. By way of example, the organism'scondition can be a malignancy and/or an infection. In one embodiment ofthe methods 1700 or 1800, the organism's condition is the AcquiredImmunodeficiency Syndrome, the portion of the target molecule is anepitope, the relationship is between an MHC-type and a mutation, and theforecast is made by searching in the vicinity of the mutation using awindow of length sufficient to include regions flanking the epitope(e.g., about 33 amino acids in length).

FIG. 19 is a schematic illustration of one example of a system 1900 thatfacilitates immunogen design. The system 1900 comprises an optimizationcomponent 1910 to determine the immunogen 1920 according to at least onecriterion 1930. The immunogen 1920 can be, for example, a set ofoverlapping sequences that are known to be and/or are likely to beimmunogenic. At least one of the sequences that are likely to beimmunogenic can be determined by analyzing associations between a hostand a pathogen at a population level. Any of the techniques describedabove can be employed to determine sequences likely to be immunogenic.

By way of example, the pathogen can be HIV and the associations can bebetween an MHC-type and escape mutations. The optimization component1910 can employ a greedy algorithm to determine the immunogen or anysuitable optimization algorithm can be used (e.g., any of the techniquesdescribed above in reference to FIGS. 1-7). For instance, a greedyalgorithm that constructs a collection of sequences which together yielda large optimization score can be employed. The greedy algorithm caniteratively insert (usually with overlap) a single epitope into thecollection of sequences such that the optimization score per unit length(where length is the total length of all the sequences) increases themost. This procedure produces a series of epitomes, each with anoptimization score and a length. External considerations can be used tochoose the optimal tradeoff of score versus length.

By way of another example, the optimization criteria can reflect theidea that if a vaccinated person with a given HLA type is exposed to agiven sequence (or collection of sequences), only the epitopes in thesequence that are (1) present in the vaccine (or that will cross reactwith CD8+ T cells stimulated by the vaccine) and (2) can be presented byan HLA molecule expressed by the patient will contribute to immuneprotection. One example of an optimization criterion is the expectednumber of cross-reacting epitopes per patient, where expectation istaken over the given population of individuals and the given populationof sequences. There are different models for determining whether CD8+ Tcells stimulated with one sequence will cross-react with anotherepitope. One example of such a model assumes that there must be an exactmatch between the sensitizing peptide and the reacting epitope. Anotherexample of a model assumes that the sensitizing peptide and the reactingepitope must differ by at most one conservative amino acid change.

The optimization component can be encoded by computer-executableinstructions stored on computer-readable media. There are numerousstrategies for delivering immunogens such as delivering each sequence inan epitome on its own viral vector, concatenating the sequences in theepitomes and delivering them on a single viral vector, and/or eachsequence can be further subdivided (e.g., to avoid immunodominance) andeach component delivered on a separate viral vector. Any of thetechniques described above and others known in the art can be used toassemble and deliver the immunogen.

FIG. 20 is a flow diagram illustrating one example of a method 2000 ofdetermining an epitome. At step 2010, a plurality of sequences arereceived. The sequences can be, for example, sequences predicted to bean epitope based on a relationship between a diverse trait of apopulation and a mutation of a pathogen. By way of another example, oneor more of the plurality of sequences comprises at least one flankingregion. At step 2020, a collection of the plurality of sequences areoptimized according to one or more criteria to determine the epitome.Optimization can be accomplished by a greedy algorithm or any suitableoptimization algorithm (e.g., any of the techniques described above inreference to FIGS. 1-7 and 19). The criteria can be length,cross-reactivity, and/or any suitable criteria or combinations thereof,such as an optimization score per unit length.

FIG. 21 is a flow diagram illustrating another method of determining anepitome. At step 2110, a plurality of sequences are received. At step2020, a collection of the plurality of sequences are optimized accordingto one or more criteria. At step 2130, a tradeoff between theoptimization score and length of the epitome is considered to determinethe epitome.

Exemplary nine (9) amino acid sequences shown in Table 7 were determinedby the systems/methods described above.

TABLE 7 Associ- ation 9Mer Escape9Mer Position HLA Protein SEQ ID NO: 1SEQ ID NO: 73 E1 A0301 Nef ANNADCAWL ATNADCAWL SEQ ID NO: 2 SEQ ID NO:74  B-1 A0301 Env NNNETETFR TNNETETFR SEQ ID NO: 3 SEQ ID NO: 75 E1A0101 Vif SKKAKGWFY SRKAKGWFY SEQ ID NO: 4 SEQ ID NO: 76 E8 A0101 GagISPRTLNAW ISPRTLNAL SEQ ID NO: 5 SEQ ID NO: 77  B-1 A0201 Pol AAVKAACWWTAVKAACWW SEQ ID NO: 6 SEQ ID NO: 78 E2 A0101 Gag PIAPGQMRE PIPPGQMRESEQ ID NO: 7 SEQ ID NO: 79  B-1 A0101 Gag NSSQVSQNY SSSQVSQNY SEQ ID NO:8 SEQ ID NO: 80 E6 B0801 Vif RDWHLGHGV RDWHLGQGV SEQ ID NO: 9 SEQ ID NO:81 E1 B0801 Pol PIWKGPAKL PLWKGPAKL SEQ ID NO: 10 SEQ ID NO: 82  B-1B0702 Env LVWRWGTML WVWRWGTML SEQ ID NO: 11 SEQ ID NO: 83 E2 A2402 GagGPSHKARVL GPGHKARVL SEQ ID NO: 12 SEQ ID NO: 84 E2 A0201 Pol YLSWVPAHKYLAWVPAHK SEQ ID NO: 13 SEQ ID NO: 85 E4 B0702 Pol KQGQDQWTY KQGQGQWTYSEQ ID NO: 14 SEQ ID NO: 86 E7 B0801 Vif HIVSPRCDY HIVSPRCEY SEQ ID NO:15 SEQ ID NO: 87 E7 A0101 Vif VDPDLADQL VDPDLADRL SEQ ID NO: 16 SEQ IDNO: 88 E2 A1101 Vif DARLVITTY DAKLVITTY SEQ ID NO: 17 SEQ ID NO: 89 E3B1501 Pol KQGQGQWTY KQGLGQWTY SEQ ID NO: 18 SEQ ID NO: 90 E5 A2402 VifHIVSPRCEY HIVSPSCEY SEQ ID NO: 19 SEQ ID NO: 91 E5 B0801 Pol PAIFQSSMTPAIFQCSMT SEQ ID NO: 20 SEQ ID NO: 92 E5 A1101 Gag RPGNFLQSR RPGNFPQSRSEQ ID NO: 20 SEQ ID NO: 92 E5 A2402 Gag RPGNFLQSR RPGNFPQSR SEQ ID NO:21 SEQ ID NO: 93 E3 B0702 Vif VDPGLADQL VDPDLADQL SEQ ID NO: 22 SEQ IDNO: 94 E2 A2402 Vpr YNEWTLELL YNQWTLELL SEQ ID NO: 23 SEQ ID NO: 95  B-1A0101 Env TLKQIVKKL MLKQIVKKL SEQ ID NO: 24 SEQ ID NO: 96 E6 A0101 VpuGDQEELSAL GDQEELAAL SEQ ID NO: 25 SEQ ID NO: 97 E5 A1101 Env EQELLELDKEQELLALDK SEQ ID NO: 26 SEQ ID NO: 98 E6 A0201 Pol ALQDSGSEV ALQDSGLEVSEQ ID NO: 27 SEQ ID NO: 99 E1 A2402 Gag KSKKKAQQA KCKKKAQQA SEQ ID NO:28 SEQ ID NO: 100 E5 A0301 Env SENFTDNAK SENFTNNAK SEQ ID NO: 29 SEQ IDNO: 101 E3 A0101 Env NAKTIIVQL NAKNIIVQL SEQ ID NO: 30 SEQ ID NO: 102 E4B0702 Env YKVVRIEPL YKVVKIEPL SEQ ID NO: 31 SEQ ID NO: 103  B-1 B0801Env IVNRVRQGY VVNRVRQGY SEQ ID NO: 7 SEQ ID NO: 104 E3 A1101 GagNSSQVSQNY NSSKVSQNY SEQ ID NO: 4 SEQ ID NO: 105  B-1 A0101 Gag ISPRTLNAWLSPRTLNAW SEQ ID NO: 32 SEQ ID NO: 106 E6 A2402 Nef PGPGIRYPL PGPGIRFPLSEQ ID NO: 33 SEQ ID NO: 107 E3 A2402 Nef LMWKFDSRL LMWRFDSRL SEQ ID NO:33 SEQ ID NO: 107 E3 B0801 Nef LMWKFDSRL LMWRFDSRL SEQ ID NO: 34 SEQ IDNO: 108  B-1 A0301 Pol DMNLPGRWK EMNLPGRWK SEQ ID NO: 35 SEQ ID NO: 109E3 B1501 Pol PLDEDFRKY PLDKDFRKY SEQ ID NO: 36 SEQ ID NO: 110 E5 A0301Pol KQLTEVVQK KQLTEAVQK SEQ ID NO: 37 SEQ ID NO: 111  B-1 A0101 PolIATESIVIW VATESIVIW SEQ ID NO: 38 SEQ ID NO: 112 E5 A2402 Pol ETAYFILKLETAYFLLKL SEQ ID NO: 39 SEQ ID NO: 113 E7 B0801 Tat QVCFIKKGL QVCFIKKALSEQ ID NO: 39 SEQ ID NO: 113 E7 B1501 Tat QVCFIKKGL QVCFIKKAL SEQ ID NO:40 SEQ ID NO: 114 E4 A0201 Tat DSQTHQVSL DSQTDQVSL SEQ ID NO: 3 SEQ IDNO: 115 E4 A0101 Vif SKKAKGWFY SKKARGWFY SEQ ID NO: 41 SEQ ID NO: 116 E7A1101 Vpr FPRPWLHGL FPRPWLHSL SEQ ID NO: 42 SEQ ID NO: 117 E5 B0801 VpuVWTIVFIEY VWTIVLIEY SEQ ID NO: 36 SEQ ID NO: 118 E3 A0301 Pol KQLTEVVQKKQLAEVVQK SEQ ID NO: 43 SEQ ID NO: 119 E2 A0301 Tat QTHQVSLSK QTDQVSLSKSEQ ID NO: 44 SEQ ID NO: 120 E3 B1501 Pol TWETWWTEY TWEAWWTEY SEQ ID NO:22 SEQ ID NO: 121 E1 A2402 Vpr YNEWTLELL YHEWTLELL SEQ ID NO: 45 SEQ IDNO: 122 E1 A0201 Nef NCLLHPMSL NSLLHPMSL SEQ ID NO: 46 SEQ ID NO: 123 E1B0801 Nef PAVRERMRR PTVRERMRR SEQ ID NO: 47 SEQ ID NO: 124 E1 A2402 EnvVQKEYALFY VKKEYALFY SEQ ID NO: 48 SEQ ID NO: 125 E2 A0301 Vif ALAALITPKALTALITPK SEQ ID NO: 49 SEQ ID NO: 126 E5 A1101 Tat PKTACTNCY PKTACNNCYSEQ ID NO: 50 SEQ ID NO: 127 E1 B0702 Nef QDILDLWVY QEILDLWVY SEQ ID NO:50 SEQ ID NO: 127 E1 B0801 Nef QDILDLWVY QEILDLWVY SEQ ID NO: 40 SEQ IDNO: 128 E3 A2402 Tat DSQTHQVSL DSQAHQVSL SEQ ID NO: 51 SEQ ID NO: 129 E7B0702 Gag SLYNTVATL SLYNTVAVL SEQ ID NO: 52 SEQ ID NO: 130 E1 A2402 EnvTAVPWNASW TTVPWNASW SEQ ID NO: 51 SEQ ID NO: 131 E2 A0101 Gag SLYNTVATLSLFNTVATL SEQ ID NO: 5 SEQ ID NO: 132 E1 A0201 Pol AAVKAACWW ATVKAACWWSEQ ID NO: 53 SEQ ID NO: 133  B-1 A1101 Rev TVRLIKFLY AVRLIKIFLY SEQ IDNO: 54 SEQ ID NO: 134 E6 A0301 Pol YAGIKVKQL YAGIKVRQL SEQ ID NO: 55 SEQID NO: 135  B-1 A1101 Gag FRNQRKTVK IRNQRKTVK SEQ ID NO: 56 SEQ ID NO:136 E4 A0101 Gag ERFAVNPGL ERFALNPGL SEQ ID NO: 57 SEQ ID NO: 137 E4B1501 Vpr AIIRILQQL AIIRTLQQL RGRQKVVSL RGRQKVVSI SEQ ID NO: 52 SEQ IDNO: 138 B-1 A0201 Env TAVPWNASW IAVPWNASW SEQ ID NO: 10 SEQ ID NO: 139E7 A2402 Env LVWRWGTML LVWRWGTLL SEQ ID NO: 46 SEQ ID NO: 140 B-1 B0801Nef PAVRERMRR SAVRERMRR SEQ ID NO: 58 SEQ ID NO: 141 E6 B1501 EnvELKNSAVSL ELKNSAISL SEQ ID NO: 10 SEQ ID NO: 142 E6 A1101 Env LVWRWGTMLLVWRWGIML SEQ ID NO: 59 SEQ ID NO: 143 B-1 A0101 Env PIDNDNTSY QIDNDNTSYSEQ ID NO: 60 SEQ ID NO: 144 E4 A0101 Pol RAMASDFNL RAMANDFNL SEQ ID NO:61 SEQ ID NO: 145 E1 A0101 Gag STLQEQIGW SNLQEQIGW SEQ ID NO: 61 SEQ IDNO: 145 E1 A1101 Gag STLQEQIGW SNLQEQIGW SEQ ID NO: 62 SEQ ID NO: 146 E5B0702 Vif RWNKPQKTK RWNKPRKTK SEQ ID NO: 63 SEQ ID NO: 147 E6 A0201 EnvLTVQARQLL LTVQARLLL SEQ ID NO: 64 SEQ ID NO: 148 B-1 A0101 Gag TVKCFNCGKIVKCFNCGK SEQ ID NO: 65 SEQ ID NO: 149 B-1 A0301 Nef RSVVGWPAV SSVVGWPAVSEQ ID NO: 66 SEQ ID NO: 150 E6 A0201 Env AVGIGAMFL AVGIGAVFL SEQ ID NO:67 SEQ ID NO: 151 E8 A1101 Nef AFHHMAREL AFHHMAREK SEQ ID NO: 41 SEQ IDNO: 152 E3 B0702 Vpr FPRPWLHGL FPRIWLHGL SEQ ID NO: 68 SEQ ID NO: 153 E8A0201 Pol SEQ ID NO: 69 SEQ ID NO: 154 E8 A0101 Env CSSNITGLL CSSNITGLISEQ ID NO: 69 SEQ ID NO: 154 E8 A2402 Env CSSNITGLL CSSNITGLI SEQ ID NO:22 SEQ ID NO: 155  B-1 A0101 Vpr YNEWTLELL HNEWTLELL SEQ ID NO: 70 SEQID NO: 156 E1 A0101 Pol VPLTEEAEL VTLTEEAEL SEQ ID NO: 71 SEQ ID NO: 157E8 A1101 Pol KLAGRWPVK KLAGRWPVT SEQ ID NO: 72 SEQ ID NO: 158 E8 A0201Env ALFYKLDVV ALFYKLDVI

SEQ ID NO: 52 SEQ ID NO: 138 B-1 A0201 Env TAVPWNASW IAVPWNASW SEQ IDNO: 10 SEQ ID NO: 139 E7 A2402 Env LVWRWGTML LVWRWGTLL SEQ ID NO: 46 SEQID NO: 140 B-1 B0801 Nef PAVRERMRR SAVRERMRR SEQ ID NO: 58 SEQ ID NO:141 E6 B1501 Env ELKNSAVSL ELKNSAISL SEQ ID NO: 10 SEQ ID NO: 142 E6A1101 Env LVWRWGTML LVWRWGIML SEQ ID NO: 59 SEQ ID NO: 143 B-1 A0101 EnvPIDNDNTSY QIDNDNTSY SEQ ID NO: 60 SEQ ID NO: 144 E4 A0101 Pol RAMASDFNLRAMANDFNL SEQ ID NO: 61 SEQ ID NO: 145 E1 A0101 Gag STLQEQIGW SNLQEQIGWSEQ ID NO: 61 SEQ ID NO: 145 E1 A1101 Gag STLQEQIGW SNLQEQIGW SEQ ID NO:62 SEQ ID NO: 146 E5 B0702 Vif RWNKPQKTK RWNKPRKTK SEQ ID NO: 63 SEQ IDNO: 147 E6 A0201 Env LTVQARQLL LTVQARLLL SEQ ID NO: 64 SEQ ID NO: 148B-1 A0101 Gag TVKCFNCGK IVKCFNCGK SEQ ID NO: 65 SEQ ID NO: 149 B-1 A0301Nef RSVVGWPAV SSVVGWPAV SEQ ID NO: 66 SEQ ID NO: 150 E6 A0201 EnvAVGIGAMFL AVGIGAVFL SEQ ID NO: 67 SEQ ID NO: 151 E8 A1101 Nef AFHHMARELAFHHMAREK SEQ ID NO: 41 SEQ ID NO: 152 E3 B0702 Vpr FPRPWLHGL FPRIWLHGLSEQ ID NO: 68 SEQ ID NO: 153 E8 A0201 Pol

ITOPIA Test Results

The amino acid sequences shown in Table 8 were tested as described belowusing the BECKMAN COULTER ITOPIA Epitope Discovery System.

TABLE 8 SEQ ID NO: Sequence 72 ALFYKLDVV 158 ALFYKLDVI 71 KLAGRWPVK 157KLAGRWPVT 70 VPLTEEAEL 156 VTLTEEAEL 22 YNEWTLELL 155 HNEWTLELL 69CSSNITGLL 154 CSSNITGLI 68 RGRQKVVSL 153 RGRQKVVSI 41 FPRPWLHGL 152FPRIWLHGL

Peptide binding, off-rate and affinity were measured according to theprotocols described in the ITOPIA Epitope Discovery System CustomerGuide. To conduct the experiments, ninety-six (96) micro-titer platescoated with MHC molecules representing the HLA alleles listed in Table 9were used to identify candidate peptides. Determinations were performedin duplicate using an ELISA plate reader. The peptide binding assay usedmeasures the ability of individual peptides to bind to the HLA moleculesunder standardized optimal binding conditions. The assay was performedfor all the test peptides across the selected HLA alleles. The testpeptides identified as “binders” were characterized further in terms ofaffinity and dissociation experiments. The off-rate assay used evaluatesthe dissociation of previously bound peptide at defined time points(expressed as the t_(1/2) value). The affinity assay used measures therelative binding affinities for the MHC molecules determined byincubating candidate peptides identified in the initial peptide bindingassay at increasing concentrations (expressed as quantity of peptideneeded to achieve 50% binding or ED50 value).

TABLE 9 Allele A*0101 A*0201 A*0301 A*1101 A*2402 B*0702 B*0801 B*1501

Peptide Binding Results

The binding of the test peptides (Table 8) to the HLA molecules (Table9) was performed at a concentration of 1.11×10⁻⁴M of peptide underoptimal, standardized test conditions. A control peptide was run inparallel on the same plate and at the same concentration as the testpeptides. The following table (Table 10) shows the results of theinitial binding by allele for each peptide. The level of binding isexpressed as a percent of positive control peptide binding for eachallele.

TABLE 10 SEQ ID NO: Sequence A*0101 A*0201 A*0301 A*1101 A*2402 B*0702B*0801 B*1501 72 ALFYKLDVV 4 80 5 1 14 3 3 4 158 ALFYKLDVI 1 62 14 1 993 5 3 71 KLAGRWPVK 0 26 114 160 11 0 4 2 157 KLAGRWPVT 2 51 9 19 8 3 4 670 VPLTEEAEL 1 5 0 1 6 4 4 4 156 VTLTEEAEL 1 26 0 1 11 0 4 7 22YNEWTLELL 0 7 0 1 35 2 3 0 155 HNEWTLELL 0 5 0 1 13 1 3 0 69 CSSNITGLL 437 0 1 26 5 4 30 154 CSSNITGLI 5 44 0 1 29 5 6 40 68 RGRQKVVSL 0 5 0 017 96 73 55 153 RGRQKVVSI 0 6 0 6 29 88 71 7 41 FPRPWLHGL 0 12 0 0 35123 19 6 152 FPRIWLHGL 0 9 0 0 41 132 39 3

Off-rate Analysis

The peptides initially identified as binders were evaluated forstability based on their ability to remain bound to MHC molecules at 37°C. at time points 0, 0.5, 1, 1.5, 2, 4, 6 and 8 hours. Curve fittingthis data was performed to yield a half-life in hours (t_(1/2))measurement for each peptide. The values obtained for each time point(in duplicate) are expressed (Table 11) as a percentage of the positivecontrol. To calculate the t_(1/2) and goodness-of-fit, as measured byr², for each peptide, a one-phase exponential decay curve, with aplateau given equal to 0, was generated using GRAPHPAD PRISM software.

TABLE 11 SEQ A*0201 A*0301 A*1101 A*2402 B*0702 B*0801 B*1501 ID t_(1/2)t_(1/2) t_(1/2) t_(1/2) t_(1/2) t_(1/2) t_(1/2) NO: Sequence (r²) (r²)(r²) (r²) (r²) (r²) (r²) 72 ALFYKLDVV 4.0 (0.76) 158 ALFYKLDVI 5.5 1.1(0.72) (0.37) 71 KLAGRWPVK 1.3 4.6 2.6 (0.89) (0.22) (0.50) 157KLAGRWPVT 1.1 1.6 (0.96) (0.63) 156 VTLTEEAEL 1.2 (0.66) 22 YNEWTLELL1.5 (0.03) 69 CSSNITGLL 0.3 1.1 3.1 (0.95) (0.13) (0.01 154 CSSNITGLI0.4 1.5 3.8 (0.91) (0.02) (0.03) 68 RGRQKVVSL 1.1 1.2 3.5 (0.05) (0.07)(0.02) 153 RGRQKVVSI 1.8 1.3 1.2 (0.00) (0.04) (0.00) 41 FPRPWLHGL 2.11.2 (0.01) (0.07) 152 FPRIWLHGL Curve 1.1 1.8 Err (0.35) (0.25)

Affinity Analysis

Dose-response curves of peptide binding to MHC were prepared by peptidetitration to determine the ED50 measurement for each peptide. The valuesobtained for the tested concentrations (in duplicate) are expressed inpercentage of the highest 9000× concentration of the positive controlpeptide (Table 12).

TABLE 12 SEQ A*0201 A*0301 A*1101 A*2402 B*0702 B*0801 B*1501 ID ED50ED50 ED50 ED50 ED50 ED50 ED50 NO: Sequence (r²) (r²) (r²) (r²) (r²) (r²)(r²) 72 ALFYKLDVV 2.E−06 (0.98) 158 ALFYKLDVI 4.E−06 9.E−07 (0.99)(0.99) 71 KLAGRWPVK 3.E−05 2.E−06 6.E−07 (0.98) (0.96) (0.99) 157KLAGRWPVT 3.E−05 2.E−04 (0.99) (0.99) 156 VTLTEEAEL 4.E−06 (0.95) 22YNEWTLELL 3.E−06 (0.75) 69 CSSNITGLL 5.E−06 1.E−04 1.E−03 (1.00) (0.91)(0.99) 154 CSSNITGLI 9.E−07 3.E−05 2.E−04 (0.91) (0.94) (0.82) 68RGRQKVVSL 5.E−06 9.E−06 4.E−04 (0.99) (0.99) (0.97) 153 RGRQKVVSI 3.E−068.E−06 7.E−06 (0.77) (0.83) (0.98) 41 FPRPWLHGL 4.E−04 2.E−06 (0.95)(0.96) 152 FPRIWLHGL 2.E−07 2.E−06 5.E−06 (0.72) (0.96) (0.98)

Multi-parametric Analysis—iScore

Multi-parametric analysis was performed to integrate the half-life (t½)and ED50 parameters in an index (iScore). The iScore (Table 13) reflectsthe capability of a peptide to reconstitute with MHC molecules in astable complex, defining its overall level of binding.

TABLE 13 SEQ ID NO: Sequence A*0101 A*0201 A*0301 A*1101 A*2402 B*0702B*0801 B*1501 72 ALFYKLDVV 0 0.484 0 0 0 0 0 0 158 ALFYKLDVI 0 0.460 0 00.339 0 0 0 71 KLAGRWPVK 0 0.084 0.606 0.834 0 0 0 0 157 KLAGRWPVT 00.129 0 0.039 0 0 0 0 70 VPLTEEAEL 0 0 0 0 0 0 0 0 156 VTLTEEAEL 0 0.1100 0 0 0 0 0 22 YNEWTLELL 0 0 0 0 0.071 0 0 0 155 HNEWTLELL 0 0 0 0 0 0 00 69 CSSNITGLL 0 0.081 0 0 0.016 0 0 0.067 154 CSSNITGLI 0 0.114 0 00.038 0 0 0.112 68 RGRQKVVSL 0 0 0 0 0 0.307 0.100 0.106 153 RGRQKVVSI 00 0 0 0.050 0.271 0.165 0 41 FPRPWLHGL 0 0 0 0 0.025 0.398 0 0 152FPRIWLHGL 0 0 0 0 0 0.421 0.189 0

FIGS. 22-23 and the following discussion is intended to provide a brief,general description of a suitable computing environment in which thevarious aspects of the subject matter described herein can beimplemented. While the subject matter has been described above in thegeneral context of computer-executable instructions of a computerprogram that runs on a local computer and/or remote computer, theinvention also can be implemented in combination with other programmodules. Generally, program modules include routines, programs,components, data structures, etc., that perform particular tasks and/orimplement particular abstract data types.

Moreover, the subject matter can be practiced with other computer systemconfigurations, including single-processor or multi-processor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based and/orprogrammable consumer electronics, and the like, each of which mayoperatively communicate with one or more associated devices. The subjectmatter can also be practiced in distributed computing environments suchthat certain tasks are performed by remote processing devices that arelinked through a communications network. In a distributed computingenvironment, program modules may be located in local and/or remotememory storage devices. However, some, if not all, of the subject mattercan be practiced on stand-alone computers.

As used in this application, the term “means” is intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a means maybe, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a server and the server can be a means. One or more means may residewithin a process and/or thread of execution and a means may be localizedon one computer and/or distributed between two or more computers. A“thread” is the entity within a process that the operating system kernelschedules for execution. As is well known in the art, each thread has anassociated “context” which is the volatile data associated with theexecution of the thread. A thread's context includes the contents ofsystem registers and the virtual address belonging to the thread'sprocess. Thus, the actual data comprising a thread's context varies asit executes.

As used in this application, the term “component” is intended to referto a computer-related entity, either hardware, a combination of hardwareand software, software, or software in execution. For example, acomponent may be, but is not limited to, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, and a computer. By way of illustration, an applicationrunning on a server and/or the server can be a component. In addition, acomponent may include one or more subcomponents. One or more componentsmay reside within a process and/or thread of execution and a componentmay be localized on one computer and/or distributed between two or morecomputers.

The subject matter described herein can operate in the general contextof computer-executable instructions, such as program modules, executedby one or more components. Generally, program modules include routines,programs, objects, data structures, etc., that perform particular tasksor implement particular abstract data types. Typically, thefunctionality of the program modules may be combined or distributed asdesired in various instances of the subject invention. The subjectmatter described herein may be embodied on a computer-readable mediumhaving computer-executable instructions for implementing various aspectsof the subject invention as well as signals manufactured to transmitsuch information, for instance, on a network.

FIG. 22 schematically illustrates an exemplary environment 2210 forimplementing various aspects of the subject invention. The environment2210 includes a computer 2212, which includes a processing unit 2214, asystem memory 2216, and a system bus 2218. The system bus 2218 couplessystem components including, but not limited to, the system memory 2216to the processing unit 2214. The processing unit 2214 can be any ofvarious available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit2214.

The system bus 2218 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 10-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 2216 includes volatile memory 2220 and nonvolatilememory 2222. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer2212, such as during start-up, is stored in nonvolatile memory 2222. Byway of illustration, and not limitation, nonvolatile memory 2222 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), or flash memory. Volatile memory 2220 includes random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such asstatic RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), doubledata rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM(SLDRAM), and Rambus Direct RAM (RDRAM), direct Rambus dynamic RAM(DRDRAM), and Rambus dynamic RAM (RDRAM).

Computer 2212 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 22 illustrates, forexample a disk storage device 2224. Disk storage device 2224 includes,but is not limited to, devices like a magnetic disk drive, floppy diskdrive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memorycard, or memory stick. In addition, disk storage device 2224 can includestorage media separately or in combination with other storage mediaincluding, but not limited to, an optical disk drive such as a compactdisk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage devices 2224 tothe system bus 2218, a removable or non-removable interface is typicallyused such as interface 2226.

In addition to hardware components, FIG. 22 illustrates software thatacts as an intermediary between users and the basic computer resourcesdescribed in suitable operating environment 2210. Such software includesan operating system 2228. Operating system 2228, which can be stored ondisk storage devices 2224, acts to control and allocate resources of thecomputer system 2212. System applications 2230 take advantage of themanagement of resources by operating system 2228 through program modules2232 and program data 2234 stored either in system memory 2216 or ondisk storage devices 2224. The subject invention can be implemented withvarious operating systems or combinations of operating systems.

A user enters commands or information into the computer 2212 throughinput device(s) 2236. Input devices 2236 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 2214through the system bus 2218 via interface port(s) 2238. Interfaceport(s) 2238 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 2240 usesome of the same type of ports as input device(s) 2236. Thus, forexample, a USB port may be used to provide input to computer 2212 and tooutput information from computer 2212 to an output device 2240. Outputadapter 2242 is provided to illustrate that there are some outputdevices 2240 like monitors, speakers, and printers, among other outputdevices 2240, which require special adapters. The output adapters 2242include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 2240and the system bus 2218. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 2244.

Computer 2212 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)2244. The remote computer(s) 2244 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer2212. For purposes of brevity, only a memory storage device 2246 isillustrated with remote computer(s) 2244. Remote computer(s) 2244 islogically connected to computer 2212 through a network interface 2248and then physically connected via communication connection 2250. Networkinterface 2248 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 2250 refers to the hardware/softwareemployed to connect the network interface 2248 to the bus 2218. Whilecommunication connection 2250 is shown for illustrative clarity insidecomputer 2212, it can also be external to computer 2212. Thehardware/software necessary for connection to the network interface 2248includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 23 is a schematic block diagram of a sample-computing environment2300 with which the present invention can interact. The system 2300includes one or more client(s) 2310. The client(s) 2310 can be hardwareand/or software (e.g., threads, processes, computing devices). Thesystem 2300 also includes one or more server(s) 2330. The server(s) 2330can also be hardware and/or software F (e.g., threads, processes,computing devices). The servers 2330 can house threads to performtransformations by employing the user interfaces, methods and systemsdescribed herein. One possible communication between a client 2310 and aserver 2330 can be in the form of a data packet adapted to betransmitted between two or more computer processes. The system 2300includes a communication framework 2350 that can be employed tofacilitate communications between the client(s) 2310 and the server(s)2330. The client(s) 2310 can connect to one or more client data store(s)2360 that can be employed to store information local to the client(s)2310. Similarly, the server(s) 2330 can connect to one or more serverdata store(s) 2340 that can be employed to store information local tothe servers 2330.

As utilized in this application, terms “component,” “system,” “engine,”and the like are intended to refer to a computer-related entity, eitherhardware, software (e.g., in execution), and/or firmware. For example, acomponent can be a process running on a processor, a processor, anobject, an executable, a program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a process and acomponent can be localized on one computer and/or distributed betweentwo or more computers.

What has been described above includes examples of the presentinvention. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe present invention, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the presentinvention are possible. Accordingly, the present invention is intendedto embrace all such alterations, modifications, and variations that fallwithin the spirit and scope of the appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the invention. In thisregard, it will also be recognized that the invention includes a systemas well as a computer-readable medium having computer-executableinstructions for performing the acts and/or events of the variousmethods of the invention.

While the subject matter described herein has been described in terms ofvarious embodiments, it will be apparent to those of skill in the artthat variations may be applied to the compositions and in the steps orin the sequence of steps of the methods described herein withoutdeparting from the concept, spirit and scope of the invention. Morespecifically, it will be apparent that certain agents which are bothchemically and physiologically related may be substituted for the agentsdescribed herein while the same or similar results would be achieved.All such similar substitutes and modifications apparent to those skilledin the art are deemed to be within the spirit, scope and concept of theinvention.

In addition, while a particular feature of the invention may have beendisclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application. Furthermore, to the extent that the terms“includes,” and “including” and variants thereof are used in either thedetailed description or the claims, these terms are intended to beinclusive in a manner similar to the term “comprising.”

1. Computer-executable instructions for performing a method offorecasting a portion of a target molecule anticipated to influence anorganism's condition, the computer-executable instructions stored oncomputer-readable storage media, the method comprising: employingpopulation data to automatically analyze one or more areas of the targetmolecule to determine the portion of the target molecule anticipated toinfluence the organism's condition, the population data pertaining to atleast one relationship between at least one diverse organism trait andthe target molecule, the target molecule comprising an HLA or an MHCmolecule and the organism's condition being Human Immunodeficiency Virus(HIV); building a plurality of patches from sequence data by iterativelyincreasing a size of a patch while decreasing an associated free energyof the patch; utilizing the plurality of patches to generate an epitomemost likely to target or identify the organism's condition, the epitomebeing a sequence that includes some or all of subsequences from the setof sequence data; and employing a cost function that measures asimilarity of the sequence data with an estimate of the epitome.
 2. Thecomputer-executable instructions of claim 1, wherein employing thepopulation data to automatically analyze one or more areas of the targetmolecule comprises learning a classifier according to the populationdata and applying the classifier to search the target molecule in avicinity of a site corresponding to a window of length that isdetermined by the at least one relationship.
 3. The computer-executableinstructions of claim 2, wherein the portion of the target moleculeanticipated to influence the organism's condition is an epitope.
 4. Thecomputer-executable instructions of claim 3, wherein the vicinity of asite determined by the at least one relationship comprises a window of alength sufficient to include flanking positions of the epitope.
 5. Thecomputer-executable instructions of claim 4, wherein the window is about33 amino acids in length.
 6. The computer-executable instructions ofclaim 1, wherein the at least one relationship is between a mutation andan MHC-type.
 7. The computer-executable instructions of claim 1, whereinthe organism's condition is one of a malignancy and an infection.
 8. Thecomputer-executable instructions of claim 7, wherein the infection isAcquired Immunodeficiency Syndrome.
 9. A method comprising: learning, bya computing device, a classifier based on population data; applying, bythe computing device, the classifier to search a portion of an MHCmolecule; forecasting, by the computing device, an anticipated influenceof the portion of the MHC molecule on a human condition, the humancondition being Acquired Immunodeficiency Syndrome (AIDS); anddesigning, by the computing device, an AIDS vaccine cocktail by:obtaining sequence data of contiguous amino acid subsequences associatedwith AIDS; building a plurality of disparate sized patches from thesequence data by iteratively increasing a size of a patch whiledecreasing an associated free energy of the patch; and aggregating theplurality of disparate sized patches to form the AIDS vaccine cocktailby adding a most frequent patch during each iteration.
 10. The method asrecited in claim 9, wherein the population data pertains to arelationship between a diverse trait of the human condition and thetarget molecule.
 11. The method as recited in claim 9, wherein theportion of the MHC molecule is an epitope.
 12. The method as recited inclaim 11, wherein the forecasting further comprises: identifying awindow of length sufficient to include regions flanking the epitope; andsearching the window of length.
 13. The method as recited in claim 12,wherein: the window of length is 33 amino acids in length; the epitopeis 9 amino acids in length; and each region flanking the epitope is 12amino acids in length.
 14. A method comprising: identifying, by acomputing device, population data pertaining to at least onerelationship between at least one diverse organism trait and a targetmolecule, the target molecule comprising an HLA or an MHC molecule andthe organism's condition being Human Immunodeficiency Virus (HIV);employing, by the computing device, population data to automaticallyanalyze one or more areas of the target molecule to determine a portionof the target molecule that is anticipated to influence the organism'scondition; receiving, by the computing device, one or more patchescorresponding to sequences of variable length, the one or more patchesbeing built from sequence data by iteratively increasing a size of apatch while decreasing an associated free energy of the patch; andutilizing, by the computing device, the one or more patches to generatea vaccine cocktail that is targeted towards the organism's condition.15. The method as recited in claim 14, wherein the employing thepopulation data to automatically analyze one or more areas of the targetmolecule includes learning a classifier according to the population dataand applying the classifier to search the target molecule in a vicinityof a site corresponding to a window of length that is determined by theat least one relationship.
 16. The method as recited in claim 14,wherein the one or more patches correspond to sequences of DNA, RNA, ora protein.
 17. The method as recited in claim 14, wherein the vaccinecocktail provides for higher epitope coverage and a larger amount oflocal diversity in comparison to vaccine cocktails generated byphylogenetic tree nodes.